Sampling Methods

Forest Health

Technology Enterprise Team

TECHNOLOGY

TRANSFER

Sampling Methods for Forest and Shade Tree 

Insects of North America, Volume 2 

Theresa A. Dellinger

Jeffrey G. Fidgen

Scott M. Salom

Forest 

Service FHTET-2010-03

April 2010

United States

Department of

Agriculture

Forest Health Technology Enterprise Team — Morgantown, West Virginia
Federal Recycling Program

Printed on recycled paper.

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service to the exclusion of others that may be suitable.

The Forest Health Technology Enterprise Team (FHTET) was created in 1995 by the Deputy Chief for State and Private Forestry, USDA Forest Service, to 

develop and deliver technologies to protect and improve the health of American 

forests. This book was published by FHTET as part of the technology transfer 

series.

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CAUTION:

PESTICIDES
Sampling Methods for Forest and Shade 

Tree Insects of North America, Volume 2

Theresa A. Dellinger 

Jeffrey G. Fidgen 

and Scott M. Salom

Acknowledgements

We are grateful to Ryan Mays of Virginia Tech for support in locating journal articles for this 

project, and to Carrie Jubb and Kara Tourje for helping assemble the glossary. In addition, 

we thank Eric Day of Virginia Tech and Drs. John L. Foltz of the University of Florida and T. 

Evan Nebeker of Mississippi State University for their valuable comments on earlier drafts of 

this manuscript. We also acknowledge the authors and journals who freely granted us permission to reproduce graphs and tables from the original articles. This review was supported with 

funding to Scott M. Salom from the USDA Forest Service, Forest Health Technology Enterprise Team (FHTET) and Region 8 agreement #05-CA-11244225-051 and a follow-up Region 

8 agreement 09-DG-11083150-014. We also thank Denise Binion for editing the manuscript in 

preparation for printing and FHTET for providing funds for printing.

i

Contents

Preface --------------------------------------- ix

Introduction --------------------------------- xi

Bud, Shoot, and Root Insects

Coleoptera: Curculionidae ------------------------------- 3

Seedling Debarking Weevil, Hylobius congener Dalla Torre, Shenkling, & Marshall 

Adult survey, pheromone-baited traps, hazard rating............................... 3

Black Vine Weevil, Otiorhynchus sulcatus (F.)

Adult survey, board and pitfall traps ........................................................ 5 

Cranberry White Grub, Phyllophaga anxia (LeConte)

Larval survey, control decision-making ................................................... 7

White Grubs, Phyllophaga spp.

Larval survey, regression equations, damage prediction.......................... 9

White Pine Weevil, Pissodes strobi (Peck)

Damage survey on spruce, sequential sampling plan............................. 11

Preliminary damage survey on spruce, optimum sample sizes...............14

Lodgepole Terminal Weevil, Pissodes terminalis Hopkins

Preliminary damage survey on spruce, optimum sample sizes.............. 14

Diptera: Cecidomyiidae---------------------------------16

Spruce Gall Midge, Mayetiola piceae (Felt)

Gall survey, sequential sampling plan, damage prediction .................... 16

 Lepidoptera: Gelechiidae ------------------------------20

Jeffrey Pine Needle Miner, Coleotechnites sp.

Larval and pupal survey with known precision, optimum sample sizes....20

Lepidoptera: Tortricidae ---------------------------------23

European Pine Shoot Moth, Rhyacionia buoliana (Denis & Schiffermüller)

Adult survey, pheromone-baited trap ..................................................... 23

Degree-day monitoring to predict cumulative adult trap catch .............. 25

Nantucket Pine Tip Moth, Rhyacionia frustrana (Comstock)

Pheromone-baited traps, degree-day monitoring, control decision-making....27 

Adult survey, pheromone-baited traps, hypothetical damage prediction.. 29

Adult survey, pheromone-baited traps, control decision-making........... 32

Defoliating Insects

Coleoptera: Chrysomelidae -----------------------------37

Cottonwood Leaf Beetle, Chrysomela scripta F.

Egg mass survey, damage prediction, control decision-making............. 37

Egg mass survey, economic injury level, control decision-making ....... 39
ii

Elm Leaf Beetle, Pyrrhalta (=Xanthogaleruca) luteola (Müller)

Egg mass survey, degree-day monitoring, optimum sample sizes, control 

decision-making ..................................................................................... 42

Egg mass survey with known precision, damage prediction, control 

decision-making ..................................................................................... 45 

Degree-day monitoring for peak density of young instars, control 

decision-making ..................................................................................... 48

Egg mass survey with known precision, degree-day monitoring, control 

decision-making ..................................................................................... 49

Hymenoptera: Diprionidae ------------------------------51

Balsam Fir Sawfly, Neodiprion abietis (Harris)

Egg mass survey, control decision-making ............................................ 51

European Pine Sawfly, Neodiprion sertifer (Geoffroy)

Adult survey, pheromone-baited trap, monitoring population trends..... 52

Hymenoptera: Tenthredinidae --------------------------54

Yellowheaded Spruce Sawfly, Pikonema alaskensis (Rohwer)

Adult survey, pheromone-baited trap, control decision-making ............ 54

Larch Sawfly, Pristiphora erichsonii (Hartig)

Damage survey, sequential sampling plan, damage prediction.............. 56

Lepidoptera: Geometridae-------------------------------60

Fall Cankerworm, Alsophilia pometaria (Harris)

Adult survey, oviposition trap ................................................................ 60

Eastern Hemlock Looper, Lambdina fiscellaria fiscellaria (Guenée)

Egg survey, oviposition trap ................................................................... 62

Western Hemlock Looper, Lambdina fiscellaria lugubrosa (Hulst)

Adult survey, pheromone-baited trap, monitoring system ..................... 64

Larval and adult survey, pheromone-baited trap, cut-branch samples, roadside sampling.......................................................................................... 66

Pupal survey, burlap band method.......................................................... 68

Egg survey, processing moss and bark samples..................................... 70

Egg survey, processing lichen samples................................................... 72

Bruce Spanworm, Operophtera bruceata (Hulst)

Adult survey, pheromone-baited trap, differentiation between congeneric 

males....................................................................................................... 74

Adult survey, pheromone-baited trap, exclusion of O. bruceata............ 76

Adult survey, pheromone-baited trap, inhibition of O. bruceata ........... 78

Adult survey, oviposition trap ................................................................ 80
iii

Winter Moth, Operophtera brumata (L.)

Adult survey, pheromone-baited trap, differentiation between congeneric 

males....................................................................................................... 74

Adult survey, pheromone-baited trap, exclusion of O. bruceata............ 76

Adult survey, pheromone-baited trap, inhibition of O. bruceata............ 78

Adult survey, oviposition trap ................................................................ 82

Larval survey, sequential sampling plan, damage prediction................. 84

Lepidoptera: Lasiocampidae ----------------------------86

Forest Tent Caterpillar, Malacosoma disstria Hübner

Adult survey, pheromone-baited trap ..................................................... 86

Lepidoptera: Lymantriidae ------------------------------87

Gypsy Moth, Lymantria dispar (L.)

Egg mass survey, timed-walk method, fixed-radius plot........................ 87

Egg mass and adult survey, pheromone-baited trap, control 

decision-making ..................................................................................... 89

Douglas-fir Tussock Moth, Orgyia pseudotsugata (McDonnough)

Egg mass and cocoon survey, artificial pupation shelter, monitoring population trends............................................................................................... 91

Adult survey, pheromone-baited trap, control decision-making ............ 94

Larval survey, monitoring population trends from nonoutbreak phase.. 96

Larval survey, midcrown estimates from lower crown samples, optimum 

sample sizes............................................................................................ 98

Review of typical phases of outbreak................................................... 102

Cocoon survey, forecasting larval populations, monitoring population 

trends .................................................................................................... 104

Comprehensive review of general sampling methods, pest management 

timeline ................................................................................................. 107

Egg mass survey, timed visual method................................................. 109

Lepidoptera: Noctuidae-------------------------------- 111

Black Army Cutworm, Actebia fennica (Tauscher)

Adult survey, pheromone-baited trap, damage prediction.................... 111

Lepidoptera: Notodontidae---------------------------- 114

Saddled Prominent, Heterocampa guttivitta (Walker)

Adult survey, pheromone-baited trap, control decision-making .......... 114

Variable Oakleaf Caterpillar, Heterocampa manteo (Doubleday)

Larval survey, control decision-making ............................................... 116

Lepidoptera: Pieridae ---------------------------------------

Pine Butterfly, Neophasia menapia (Felder & Felder)

Egg survey, damage predictions........................................................... 117

Lepidoptera: Psychidae -------------------------------- 119
iv

Bagworm, Thyridopteryx ephemeraeformis (Haworth)

Larval survey, aesthetic injury level, control decision-making............ 119

Lepidoptera: Saturniidae ------------------------------ 121

Orangestriped Oakworm, Anisota senatoria (J.E. Smith)

Frass survey, aesthetic injury level, control decision-making.............. 121

Egg mass and young larval survey, damage prediction........................ 123

Lepidoptera: Tortricidae ------------------------------- 125 

Western Blackheaded Budworm, Acleris gloverana (Walsingham)

Egg survey, hot water extraction technique.......................................... 125

Larval survey, 3-tree sampling ..............................................................127

Egg survey with known precision, hot water extraction technique, damage 

predictions ............................................................................................ 129

Two-year-cycle Spruce Budworm, Choristoneura biennis Freeman

Damage survey, second year damage predictions................................ 132

Spruce Budworm, Choristoneura fumiferana (Clemens)

Comprehensive review of egg mass sampling methods........................135

Damage survey, optimum sample sizes................................................ 142

Larval survey, whole branch from midcrown level.............................. 146

Comprehensive review of sampling methods of all life stages............ 148

Egg mass survey, sequential sampling plan.......................................... 153

Original development of sampling methods ........................................ 156

Larval survey, density per kg fresh weight method, statistical error corrections ...................................................................................................... 159

Adult survey, pheromone-baited trap, monitoring population trends... 162

Adult survey, pheromone-baited trap, monitoring when to start larval 

surveys.................................................................................................. 164

Egg mass and larval survey, aerial damage survey, damage 

predictions ............................................................................................ 166

Western Spruce Budworm, Choristoneura occidentalis Freeman

Egg mass survey, monitoring population trends................................... 170

Larval survey, sampling criteria ........................................................... 172

Larval survey, mid-crown branch tip estimates.................................... 174

Egg mass survey, branch tip estimate................................................... 177

Larval survey, midcrown estimates from lower crown samples, optimumsample sizes.......................................................................................... 179

Defoliation survey, damage predictions............................................... 182

Pupal survey with known precision...................................................... 184

Jack Pine Budworm, Choristoneura pinus pinus Freeman

Risk-rating susceptible stands .............................................................. 186 

Egg mass survey, damage predictions.................................................. 188

Defoliation survey with known precision, optimal sample sizes......... 190
v

Oak Leaftier, Croesia semipurpurana (Kearfott)

Egg survey, damage predictions........................................................... 192

Spruce Bud Moth, Zeiraphera canadensis Mutuura & Freeman

Larval survey, sequential sampling plan............................................... 194

Thysanoptera: Thripidae------------------------------- 198 

Introduced Basswood Thrips, Thrips calcaratus Uzel

Comparison of three different survey methods .................................... 198

Miscellaneous Insects

Indicator plant phenology, control decision-making ............................ 203

Piercing and Sucking Insects and Mites

Acari: Tetranychidae ----------------------------------- 211

Spruce Spider Mite, Oligonychus ununguis (Jacobi)

Degree-day monitoring, control decision-making................................ 211

Adult survey, beat sheet method, damage prediction ........................... 213

Adult survey, sequential sampling plan with known precision ............ 215

Hemiptera: Adelgidae---------------------------------- 217

Balsam Woolly Adelgid, Adelges piceae (Ratzeburg)

Adult survey, optimum sample sizes, control decision-making ........... 217

Hemlock Woolly Adelgid, Adelges tsugae Annand

Adult survey, binomial sampling plan with known precision ............. 221

Adult survey, binomial sampling plan, control decision-making......... 226

Hemiptera: Aphididae---------------------------------- 229

Black Larch Aphid, Cinaria laricifex (Firch)

Nymphal and adult survey, sequential sampling plan with known 

precision ............................................................................................... 229

Balsam Twig Aphid, Mindarus abietinus Koch

Degree-day monitoring, control decision-making................................ 231

Woolly Pine Aphid, Schizolachnus piniradiatae (Davidson)

Egg mass survey, control decision-making ...........................................233

Hemiptera: Cercopidae -------------------------------- 234

Saratoga Spittlebug, Aphrophora saratogensis (Fitch)

Alternate host survey, damage predictions........................................... 234

Hemiptera: Diaspidae ---------------------------------- 237

Obscure Scale, Melanaspis obscura (Comstock)

Degree-day monitoring, control decision-making................................ 237

Hemiptera: Drepanosiphidae-------------------------- 239

Birch Aphids, Multiple species

Nymphal and adult survey, aesthetic injury level, optimal sample sizes, control decision-making............................................................................. 239

Hemiptera: Tingidae ----------------------------------- 244
vi

Sycamore Lace Bug, Corythucha ciliata (Say)

Nymphal and adult survey, fixed sampling plan with known 

precision .............................................................................................. 244

Seed and Cone Insects

Diptera: Anthomyiidae--------------------------------- 249

Spiral Spruce Cone Borer, Strobilomyia neanthracina Michelsen

Egg survey, control decision-making ................................................... 249

Hemiptera: Coreidae ----------------------------------- 250

Western Conifer Seed Bug, Leptoglossus occidentalis Heidemann

Nymphal and adult survey, damaged seed survey, loss estimate.......... 250

Damaged seed survey, immunoassay method ...................................... 253

Nymphal and adult survey, damage predictions................................... 255

Lepidoptera: Tortricidae ------------------------------- 249

Spruce Seed Moth, Cydia strobilella (L.)

Egg survey, control decision-making ................................................... 249

Multiple Species

Damaged cone and seed survey, optimal sample sizes......................... 257

Damaged cone and seed survey, optimal timing .................................. 260

Damaged cone and seed survey, cut cone surface method, harvest decisionmaking .................................................................................................. 262

Wood- and Bark-boring Insects

Coleoptera: Buprestidae ------------------------------- 267

Bronze Birch Borer, Agrilus anxius Gory

Degree-day monitoring, control decision-making................................ 267

Emerald Ash Borer, Agrilus planipennis Fairmaire

Adult survey, lure-baited trap ............................................................... 269

Adult survey, purple box trap ............................................................... 271

Adult survey, purple prism trap ............................................................ 273

Coleoptera: Cerambycidae ---------------------------- 277

Red Oak Borer, Enaphalodes rufulus Haldeman

Population estimation, dissected bolt method ...................................... 277

Population estimation within stand ...................................................... 279

Population estimation, nondestructive method..................................... 281

Coleoptera: Curculionidae ---------------------------- 284
vii

Mountain Pine Beetle, Dendroctonus ponderosae Hopkins

Damage survey, line transect method................................................... 284

Population estimation, fixed-bark sample ............................................ 287

Douglas-fir Beetle, Dendroctonus pseudotsugae Haldeman

Adult survey with known precision, sequential sampling plan, 

control decision-making ....................................................................... 289

Spruce Beetle, Dendroctonus rufipennis (Kirby)

Brood survey, monitoring population trends........................................ 291

Adult survey, pheromone-baited trap, monitoring population trends... 292

Scolytid Beetles

Adult survey, Lindgren funnel traps..................................................... 294

Glossary ------------------------------------297

References --------------------------------305

Permission Acknowledgements -----327

Index ----------------------------------------329

Preface

The genesis of this project came from an undergraduate classroom discussion around 12 

years ago. A student in a shade tree pest management course taught at Virginia Tech asked 

if a compilation of sampling methods for estimating insect populations was available 

for reference. At that time no such resource existed for the average forester or arborist. 

Recognizing the need for an unambiguous and comprehensive tool, the original volume of 

Sampling Methods for Forest and Shade Tree Insects of North America was published by 

FHTET in 2001 (FHTET 2001-01). The information included in this publication was also 

made available online and can now be accessed at http://www.sampforestpest.ento.vt.edu/ 

(note this is a different URL from the one originally published in Vol. 1). 

The responses to both the initial publication and website have been consistently positive. 

However, it was not long before we realized that the first volume probably did not cover all 

available publications that met our criteria of developing or describing sampling procedures 

to help make IPM assessments and decisions. In addition, many new publications became 

available after the publication of the first volume. As a result, a second volume has been 

produced that has nearly as many publications and summaries as the first volume. The 

updated website will incorporate all of the summaries from both volumes into one outlet.

The usefulness of this product is as important now as it was when it was first conceived. 

The use of the Internet has exploded since 2001 and the publishing industry has adapted to 

these changes. Many more federal publications are available online than previously. Most 

publishers provide all recent and often many older journal articles online. But if someone 

searching for this information is not associated with a research library, access to a specific 

journal article usually requires a steep fee. 

Someone new to the field of estimating insect populations will greatly benefit from this 

resource that summarizes much of the sampling information available for both common and 

infrequent tree pests. In addition, many experienced natural resource professionals involved 

in making pest management decisions may benefit by reviewing the basic summaries that are 

provided here for some of the more complex sampling techniques. Furthermore, as noted in 

the preface to Volume 1, this compilation helps identify key forest pests for which sampling 

procedures for use in IPM are lacking and for which further resource is warranted. 

Again, we are pleased to present the results of many years of effort and we hope that you find 

this compendium a useful tool for managing insect pest populations of forest and shade trees.

Theresa A. Dellinger 

Jeffrey G. Fidgen 

Scott M. Salom

ix

Introduction

Sampling to estimate forest insect pest populations or damage levels is essential in decisionmaking and efficacy evaluation of any forest pest management program. Prior to Fettig 

et al. (2001) and http://www.sampforestpest.ento.vt.edu/, a comprehensive presentation of 

sampling procedures for forest pests was not available. With the hardcopy compendium of 

summaries and the associated online database, sampling procedures were easier to find and 

understand and made more accessible for application in management situations. 

The original publication required an exhaustive library search that yielded over 300 publications. Of these, 123 were considered appropriate for inclusion, using the criterion that the 

publication had to describe a sampling procedure that can be used directly or indirectly in 

pest management decision-making. In this second volume, using the same criterion, an additional 325 publications were identified and reviewed. From these, over 100 were selected 

for inclusion in this volume. Some of the articles generated a summary for more than one 

pest species, and some summaries contain content from more than one research article. All 

together, Volume II contains 115 summaries organized by feeding guild (Table 1). Sampling 

procedures for defoliating insects continues to dominate among the feeding guilds. 

Table 1. The number of summaries within each category in Vol. 2

Feeding group or guild Number of summaries

Bud, shoot, root insects 14

Defoliating insects 67

Miscellaneous insects 1

Piercing and sucking insects and mites 13

Seed and cone insects 7

Wood- and bark-boring insects 13

Each summary is formatted as in the original publication and includes: the citation, objective, abstract, sampling procedure (description of how to use it), notes (caveats), and references used in the summary. References beginning with an asterisk (*) were reviewed in the 

original publication and those beginning with a pound sign (#) were reviewed in this volume. 

We have included a more robust glossary section to help those unfamiliar with some of the 

biological and statistical terms.

No judgment was made on which sampling articles are the most appropriate to use for a 

particular pest. This compilation represents an archival record on what has been published 

previously in the literature and the user must decide how to use the techniques presented in 

each summary. Sometimes it may be appropriate for the user to consult the original article. 

If you do plan to use a particular procedure summarized here, we recommend that you read 

xi
the original article and verify that it suits your needs. 

Reference

Fettig, C. J., J. G. Fidgen, Q. C. McClellan, and S. M. Salom. 2001. Sampling methods for 

forest and shade tree insects of North America. FHTET-2001-01. Morgantown, WV: 

U.S. Department of Agriculture, Forest Service, Forest Health Technology Enterprise 

Team; 273 pp

xii
1

BUD, SHOOT, AND ROOT INSECTS
2
3

Seedling Debarking Weevil 

Hylobius congener Dalla Torre, Shenkling, & Marshall

Coleoptera: Curculionidae

Pendrel, B. A. 1990. Hazard from the seedling debarking weevil: a revised key to predicting 

damage on sites to be planted. Tech. Note 236. Forestry Canada, Maritimes Region; 4 p.

Objective

To revise a hazard key, originally published in 1987 (Pendrel 1987), for predicting damage 

by H. congener on 1-yr-old cuts to be planted. 

Abstract

The seedling debarking weevil, H. congener Dalla Torre, Shenkling and Marshall, is a serious regeneration pest of newly planted seedlings on 1-yr-old clear cuts in the Maritime 

Provinces of Canada. Adult weevils girdle the main stem of planted seedlings, killing them 

quickly. A dichotomous key was devised that integrates variables such as percentage of 

ground cover and mineral soil, basal area of regenerating tree stems, and density of trapped 

adults. The key determines weevil hazard and aids management decision-making on oneyear-old cuts as well as sites where the pre-harvest softwood content was ≥25% or in areas 

with a history of H. congener activity.

Sampling Procedure

Standardized pitfall traps, baits for H. congener, and instructions for their use are available 

from Research and Productivity Council (Fredericton, NB, Canada). Planting sites should 

be assessed 3–4 times in late May or early June. The first visit is necessary to evaluate the 

percent ground cover with feather moss, which provides hiding places for weevils, and 

exposed mineral soil, which discourages weevil activity. Using a meter stick, calculate the 

density of regenerating stems in m2

 plots as averaged from several plots in the site. Install 

at least three pitfall traps per site. Check traps daily for three days and average the number 

of trapped weevils for the site. Trapping can be discontinued after the first day if an average 

of two weevils is found in each trap. Trapping can be discontinued after the second day if 

the cumulative average number of weevils per trap is two. In both instances the minimum 

number of weevils needed to follow the key has been trapped. Otherwise, reference Table 1 

to determine weevil hazard. 

Note

The author did not provide details regarding the type of pitfall trap and bait used in this study. 
4

This key was developed for sites in the Maritimes and so it should be, at least, used with 

caution in other geographic areas where this insect is a problem. The key should be used in 

the first spring following a harvest, which may not be operationally compatible with a spring 

planting. The key may be more suitable for use in conjunction with fall plantings.

Table

Table 1. Key to predicted damage from Hylobius congener in one-year-old cuts.

1 a GROUND COVER <10% FEATHER MOSS 2

 b GROUND COVER >10% FEATHER MOSS 4

 

2 a EXPOSED MINERAL SOIL <10% 3

 b EXPOSED MINERAL SOIL >10% 5

 

3 a REGENERATING STEMS <5/M2 4

 b REGENERATING STEMS >5/M2 5

 

4 a WEEVILS TRAPPED <0.3/NIGHT 6

 b WEEVILS TRAPPED >0.3/NIGHT 7

 

5 a WEEVILS TRAPPED <0.5/NIGHT 6

 b WEEVILS TRAPPED >0.5/NIGHT 7

 

6 LOW HAZARD. MORTALITY 0–5%

 

7 MODERATE TO HIGH HAZARD. MORTALITY >5%

Table 1 reproduced with the permission of the Minister of Public Works and Government Services, granted April 14, 2009.
5

Black Vine Weevil 

Otiorhynchus sulcatus (F.)

Coleoptera: Curculionidae

Reding, M. E.; Klein, M. G.; Brazee, R. D.; Krause, C. R. 2003. Research on black vine 

weevil and white grubs in ornamental nurseries in Ohio by USDA-ARS. Wooster, OH: US 

Department of Agriculture, Agricultural Research Service Extension Circular. 189: 89-93.

Objective

To develop effective traps for monitoring emerging adult O. sulcatus.

Abstract

The black vine weevil, Otiorhynchus sulcatus (F.), can be a serious pest of ornamental trees, 

especially those grown in containers or larger field-planted trees in nurseries. Larvae feed 

on the roots and girdle the main stem resulting in poor growth rates and mortality in extreme 

cases. Control measures against O. sulcatus are initiated when adults are first found, but they 

are often difficult to detect. Adult O. sulcatus rest in concealed locations during the day and 

are active at night. 

Black vine weevil is a serious pest of trees in the genus Taxus (yews). The efficiency of 

three trap types was tested for capturing adult weevils in a typical but unsprayed field nursery of Taxus. Two of these traps were effective. The first trap is a 900 cm2

 x 2.54 cm thick 

board with 0.6 cm grooves cut into one face. Placed grooved-side down, adult O. sulcatus

hide in the grooves during the day. The second is a pitfall trap made from two 500 ml plastic 

drinking cups. The former trap caught weevils earlier than the latter one. Moreover, foliage beating to dislodge adults was done concurrently with the placement of traps in the field. 

The board trap captured adults earlier than either the pitfall trap or the traditional means of 

detecting adults by beating foliage. However, the pitfall trap still captured adults several 

days before they were found by the beating method.

Sampling Procedure

Cut a 2.54 cm board into 30 X 30 cm squares. On one side of each 30 x 30 cm piece, cut a 

series of 0.6 cm grooves. No information was given as to their spacing and interested users 

should contact the authors for this information. Ten equally spaced grooves may be sufficient. Place traps grooved side down beneath the crown of randomly selected yews. This 

provides an attractive, convenient hiding place for adults.

The second trap consists of two 500 ml plastic drinking cups. The first cup is placed in a 
6

hole beneath the crown of a randomly selected yew so that the lip of the cup is flush with 

ground level. Coat the inside rim of the second cup with grease to form an escape barrier and 

set the cup inside the first one.

Check traps regularly and initiate control measures when adult O. sulcatus are found.

Notes

These traps have not been used operationally and so they should be used with caution. The 

authors recommend further testing in commercial fields under normal management practices 

and with varying pest densities. No details were given as to the spatial deployment or number of traps needed to give reasonably accurate and precise information on weevil populations for management purposes.
7

Cranberry White Grub 

Phyllophaga anxia (LeConte)

Coleoptera: Scarabaeidae

Williamson, R. C. 2004. White grub (Coleoptera: Scarabaeidae) population density in relation to root damage to Fraser fir seedlings in transplant beds. Journal of Environmental 

Horticulture 22: 85–87.

Objective

To develop a damage threshold for P. anxia by relating larval density to root damage and 

seedling weight of Fraser fir, Abies fraseri (Pursh) Poir.

Abstract

White grubs (Phyllophaga spp.) is a widespread pest of the roots of many species of vascular 

plants. Larvae chew and girdle roots, impairing growth rates and easily killing plants. More 

specifically, cranberry white grub, Phyllophaga anxia (LeConte), is a pest of Christmas tree 

nursery stock in transplant beds.

Damage by second instar P. anxia on Fraser fir was investigated by experimentally manipulating densities of grubs and measuring the effect of increasing larval densities on root 

damage and plant weight. A density of approximately 31 larvae per m2

 (3–4 larvae per ft2) 

resulted in significant root damage and a 50% reduction in seedling weight. Controls were 

recommended if the density of P. anxia larvae were at or above this threshold.

Sampling Procedure

Because this study manipulated grub densities experimentally, no details were given for a 

defined sample unit, the number of sample units to collect, or a specific procedure to collect 

them. The methodology of Ives and Warren (1965) for sampling white grubs (Phyllophaga

spp.) could be used for this purpose. Briefly, remove all soil in a 30 by 30 cm surface area 

to the depth of 10 cm (a total volume of 9,000 cm3

) from the transplant bed. Pass portions 

of this soil sample one at a time through a screen to collect the grubs. Multiple samples of 

9,000 cm3 soil each should be taken from different areas of the transplant bed. Tally the cumulative number of white grubs found in all the soil samples and calculate the larval density 

per m2 based on the total area sampled. Control is warranted if the density of white grubs 

averages 31 larvae or more per m2

 in a transplant bed.
8

Notes

Although P. anxia larvae were used specifically in this study, the author suggests that this 

threshold is useful for any Phyllophaga spp. larvae. Ives and Warren (1965) recommended 

including all white grubs found in the soil samples as the larvae are difficult to identify to 

species. 

Reference

* Ives, W. G. H.; Warren, G. L. 1965.Sequential sampling for white grubs. Canadian 

Entomologist 97: 596-604.
9

White Grubs 

Phyllophaga spp.

Coleoptera: Scarabaeidae

Fowler, R. F.; Wilson, L. F. 1971. White grub populations, Phyllophaga spp., in relation to 

damaged red pine seedlings in Michigan and Wisconsin plantations (Coleoptera: Scarabaeidae). Michigan Entomologist 4: 23–28.

Objective

To predict seedling mortality of red pine, Pinus resinosa Soland., based on larval density of 

white grubs, predominantly comprised of Phyllophaga spp.

Abstract

White grubs (Phyllophaga spp.) are a widespread pest of the roots of many species of 

vascular plants. Larvae chew and girdle roots, impairing growth rates and easily killing 

young plants.

White grub populations were studied in red pine, Pinus resinosa Soland., plantations in 

national forests in the Great Lakes region. Root damage was accessed on seedlings and 

related to larval density using linear regression. Damage levels were positively related 

to larval density, particularly when separated by instar (r2 = 0.78). This relationship is 

expressed in the following formula:

y = 0.94 + 2.30(l-e-0.03x)

where x = the feeding index and y = the damage index (modified from Johnston & Eaton 

1939). The feeding index (FI) used in the above equation is FI = [N1 + 2N2 + 4N3 + 8N4]/n 

where N1, N2, etc. represent the number of larvae in instar 1, 2, 3, or 4, and n = the mean 

number of larvae sampled at the study site. The FI assumes that each instar eats twice as 

much as the previous instar. The damage index is defined as:

Damage Score Extent of injury to roots

1 No grub injury

2 ≤33% of fibrous roots damaged

3 34–66% of fibrous roots damaged

4 67–99% of fibrous roots damaged; some seedlings stripped of 

fibrous roots but some regrown root tips

5 100% of fibrous roots destroyed or tap root severed above the 

fibrous roots

The percentage of red pine seedlings damaged at the study site can also be predicted from the 
10

feeding index using the equation y = 5.34 + 65.41(l-e -0.04x), where x = the feeding index and 

y = the percentage of seedlings damaged. Finally, the damage index was positively related 

(r2 = 0.89) to the percentage of damaged red pine seedlings and can be predicted using the 

equation y = 1.08 + 0.03x, where y = the damage index and x = the percentage of seedlings 

with damaged roots. This formula provides a reasonable damage estimate for Phyllophaga

spp. larvae when >10% but <90% of the seedlings are damaged, without the need to actually 

score root damage on dug seedlings. 

A threshold of 0.5 larvae per 0.09 m2 or 0.03 m3 soil has been suggested as a damage 

threshold for Phyllophaga spp. larvae, but the authors suggest this should be lowered. Based 

on their study, a density of 0.5 larvae per 0.03 m3 would result in 16–34% seedling mortality 

in the subsequent growing season, including approximately 4% loss due to other mortality 

factors. A threshold of ≥0.25 larvae per 0.09 m2 is suggested if seedling mortality should be 

≤20%.

Sampling Procedure

Ideally damage estimates should be made before seedlings are planted or soon afterwards. 

Establish 4-5 parallel transects across the plantation. In the summer, sample larval densities 

by digging a 0.03 m3 soil sample enclosed by a 30.5 x 30.5 cm metal quadrant. Take 4–6 

soil samples evenly spaced along each transect. Sift each soil sample through a 0.64 cm 

mesh screen. Identify larvae to Phyllophaga spp. larvae and tally the number of each instar 

present. Calculate the mean density of larvae per 0.03 m3

 soil. 

Calculate the Feeding Index (FI) using the equation FI = [N1 + 2N2 + 4N3 + 8N4]/n where 

N1, N2, etc. represent the number of larvae in instar 1, 2, 3, or 4, and n = the mean number of 

larvae sampled at the study site. Predict the percentage of damaged red pine seedlings using 

the equation y = 5.34 + 65.41(l-e-0.04x), where x = the feeding index and y = the percentage 

of seedlings damaged. Predict the extent of larval damage using the equation y = 0.96 

+ 2.07(1-e-2.92x), where x = the damage index and y = the mean density of Phyllophaga

spp. larvae per 0.03 m3 soil. Consult Table 1 to relate the calculated damage index to the 

predicted extent of injury to seedling roots.

If seedlings are dug and examined for feeding injury, the damage index can be predicted 

using the equation y = 1.08 + 0.03x, where y = the damage index and x = the percentage of 

seedlings with damaged roots. Sample 20–90 seedlings near each location where the soil 

was sampled for Phyllophaga spp. larvae. 

Reference

Johnston, H. R.; Eaton, C. B. 1939. White grubs in forest nurseries of the Carolinas. Rep. E-486. 

U.S. Department of Agriculture, Bureau of Entomology and Plant Quarantine; 9 p.
11

White Pine Weevil on Spruce 

Pissodes strobi (Peck)

Coleoptera: Curculionidae

Alfaro, R. I. 1995. A sequential sampling system for the white pine weevil, Pissodes strobi

(Coleoptera: Curculionidae). Journal of the Entomological Society of British Columbia 92: 

39–43.

Alfaro, R. I. 1998. White pine weevil, Pissodes strobi: risk factors, monitoring and management. Tech. Transfer Note No. 4. Victoria, B.C.: Natural Resources Canada, Canadian 

Forest Service, Pacific Forestry Centre; 4 p.

Objective

To describe a sequential sampling system for P. strobi damage in plantation-grown spruce and 

review risk factors for this pest. 

Abstract

White pine weevil, Pissodes strobi (Peck), is an important pest of a number of spruces, Picea

spp., and pines, Pinus spp., in North America. Larvae tunnel down through the terminal 

leader of host trees, destroying the apical dominance of the shoot. The resulting tree deformation reduces the commercial value of sawlogs and landscape trees. Stands of even-aged 

spruce trees, such as plantations or natural regeneration following a fire, are susceptible to 

attack when juvenile and are most susceptible when 10 and 30 years old. Attack by P. strobi

typically does not kill older trees, but does reduce their growth rate.

A sequential sampling plan was developed to rapidly determine infestation levels of P. strobi

in spruce. The method requires a maximum of 60 randomly selected spruce trees. All 60 

trees must be sampled if the infestation ranges between 10–19%, but only 19 trees need be 

sampled sequentially to classify a stand as lightly infested. Infestations are considered light, 

moderate and severe if <10, 10–19, and ≥20% of the trees sampled have weevils. Young 

trees with accessible terminal leaders should be treated when warranted by pest pressure. 

Susceptibility of spruce stands to P. strobi can be minimized through careful selection of 

stand location, stand composition, and frequent monitoring when trees are young (≤30 years 

old). 

Sampling Procedure

Sequential sampling plan (described in Alfaro 1995, 1998): Divide the stand into a grid (e.g., 

a 5 m X 5 m grid) with assigned X, Y coordinates. Randomly select 60 X, Y coordinate 

points from the grid; a random number generator would be useful for this. Do not duplicate 

numbers or replace them in the selection process. Number each coordinate point sequentially 
12

from 1 to 60 in the order that they were selected.

Divide the list of coordinate points into 1–30 and 31–60. Beginning with 1–30, check the 

tree closest to the coordinate point in the stand for weevil damage. Move through the stand 

in an efficient manner and tally the number of trees with weevil damage; coordinant points 

do not need to be assessed sequentially. Plot the cumulative number of infested trees against 

the cumulative number of trees sampled using Fig. 1. If the slope of the plotted data crosses 

either decision line, then the stand can be classified as either lightly or severely infested without additional sampling. If the slope does not cross a decision line and remains in the moderate zone, continue sampling the second group of 31–60 coordinate points. After sampling 

the entire second group, continue plotting the cumulative tally of infested trees against the 

cumulative number of trees sampled in the same manner. The stand can then be classified as 

having a light, moderate, or severe infestation after sampling a maximum of 60 trees in this 

manner. 

Stand classification Level of observed damage

Light <10%

Moderate Between 10 and 19%

Severe ≥20%

Risk factor evaluation (Alfaro1998): Known risk factors for P. strobi include even-aged 

stands of spruce, host species with long leaders, spruce stands between 10 and 30 years old, 

and thinned stands with reduced density. Sites that accumulate more than 785 degree days 

above 7.2°C over a year also encourage weevil attack. Dominant deciduous trees can shade 

plantings, reducing the risk of attack.

Note

This sampling plan was developed for spruce trees and may not be applicable for pines attacked by P. strobi.
13

Figure 1. Parallel decision lines for a sequential sampling system for the white pine weevil, 

Pissodes strobi (Alfaro 1995).

Figure 1 reprinted with permission from the author, granted April 6, 2009.
14

Pissodes Terminal Weevils 

White Pine Weevil, Pissodes strobi (Peck)

Lodgepole Terminal Weevil, Pissodes terminalis Hopkins

Coleoptera: Curculionidae

Fletcher, V. E. 1986. Development of sampling guidelines for estimating the proportion of 

weeviled trees on a plantation (plus errata). Forest Service Internal Rpt. PM-PB-18. Victoria, B.C.: British Columbia Ministry of Forests; 19 p.

Objective

To estimate the density of leaders damaged by P. strobi and P. terminalis at the 80 and 95% 

confidence levels in coastal plantations of Sitka spruce trees in British Columbia. 

Abstract

White pine weevil, Pissodes strobi (Peck), is an important pest of a number of spruces, Picea

spp., and pines, Pinus spp., in North America. Larvae tunnel down through the terminal 

leader of host trees, destroying the apical dominance of the shoot and destroying two years’ 

growth. The resulting tree deformation reduces the commercial value of sawlogs and landscape trees. Stands of even-aged spruce trees, such as plantations or natural regeneration 

following a fire, are susceptible to attack when juvenile and are most susceptible between 10 

and 30 years old. Attack by P. strobi typically does not kill older trees, but does reduce their 

growth rate. The related lodgepole terminal weevil, Pissodes terminalis Hopkins, produces 

similar damage in current-year growth.

Systematic sampling of trees in plantation rows provides an estimation of the percentage 

of trees attacked by P. strobi and P. terminalis. Systematic sampling offers the benefits of 

uniformly covering the plantation while detecting random and aggregated attacks. Sampling 

guidelines were developed for Pissodes spp. in Sitka spruce based on visual observation of 

terminal damage along set transects across a plantation. Managers can use this sampling 

scheme to determine the extent of an infestation before applying a residual insecticide in the 

spring when adults are feeding but before oviposition occurs, or to evaluate the efficacy of 

chemical application.

Sampling Procedure

Use Table 6 to determine the sample size (number of transects) needed given a required 

bound on the error of estimation and a set confidence interval of 80 or 95%. Twenty 

transects per plantation is a general recommended guideline. Determine a “transect interval” 

by dividing the total number of rows at the plantation by the number of transects required. If 

the plantation has 150 rows and 20 transects are desired, then the resulting transect interval is 
15

8 after rounding up. Randomly choose a number between 1 and 8 as a starting row, such as 

5. Set the first transect at row 5, the next at row 13, then rows 21, 29, etc. 

Walk along transects and record the number of healthy trees and trees with Pissodes spp. 

damage on rows to either side. Ignore dead trees or damage from previous years. If little 

brush is present across the plantation, the observer can survey two rows on either side while 

walking down each transect. Transects may represent 1, 2, or 4 rows of trees, but a similar 

number of trees should be surveyed on each transect. Calculate the total number of trees with 

leader damage and the total number of trees observed from all transects. Estimate the average percentage of infested trees by dividing the cumulative number of trees observed with 

damage by the cumulative number of observed trees and multiplying by 100.

Notes

This is a preliminary sampling plan that should be used with the understanding that it could 

be improved with additional data. Furthermore, the plan was developed for spruce trees and 

may not be applicable for pines attacked by P. strobi or P. terminalis.

Table 6. Sample size calculations for estimating proportion of infested trees. Between 

transect variation ( S2

 = 200) estimated from previous field data.

Bound on Error of 

Estimation

No. of Transects in 

Population

Average Transect Size 

(no. of trees)

No. of Transects Required (sample size)

80% Confidence Limits

0.10 100 100 3

0.10 150 100 3

0.10 200 100 3

0.05 100 100 12

0.05 150 100 12

0.05 200 100 12

95% Confidence Limits

0.10 100 100 7

0.10 150 100 8

0.10 200 100 8

0.05 100 100 24

0.05 150 100 26

0.05 200 100 28

Table 6 was modified to reflect the changes made in the Errata for this publication. 

The table is reproduced with the permission of the Forest Establishment Officer, Ministry of 

Forests and Range, Victoria, BC, granted May 6, 2008.
16

Spruce Gall Midge

Mayetiola piceae (Felt)

Diptera: Cecidomyiidae

Brandt, J. P. 2000. A sequential sampling plan for classification of damage caused by spruce 

gall midge (Mayetiola piceae [Felt]). Forest Management Note 65. Edmonton, Alberta, Canada: Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre; 7 p.

Objective

To develop a sequential sampling plan for classifying M. piceae damage on white spruce as 

light, moderate, or severe.

Abstract

The spruce gall midge, Mayetiola piceae (Felt), is a periodically severe pest of white spruce, 

Picea glauca (Moench) Voss, in Canada and the northern U.S. Larvae bore into new shoots, 

causing a noticeable gall in as little as 10 days. Damage by M. piceae is seen as reduced 

growth, deformation, and eventual mortality of twigs. Very little is known about M. piceae, 

except for work published by Felt (1926) and Smith (1952). A sequential sampling plan was 

devised to classify populations into categories based on the number of galled current-year 

shoots.

The type I and II errors for sequential sampling were set at 10% such that there was a 1 in 10 

chance that the classification was inappropriate. Populations of M. piceae were classified as 

light, moderate and severe if the mean number of galled current-year shoots per branch was 

≤3, 4 to 16, and ≥24, respectively. At least 28 uninfested trees must be sampled to classify 

a site as having light damage, but sites could be classified as having severe damage after 

sampling fewer trees.

Sampling Procedure

Sample white spruces when galls are visible on current-year shoots. Randomly select stands 

from the area of concern, as well as trees from the stands of concern and branches from 

sample trees. Select only dominant or co-dominant trees along a transect with a random 

start location. From each tree along the transect, remove one branch from each of the lower, 

middle, and upper crown. Also remove an additional branch from a crown level that is selected at random. 

Average the number of galls among the four branches and reference Table I. If the cumulative number of galled current year shoots remains within the continue sampling bands, select 
17

the next tree and sample four branches as described above. Add this total to the previous one 

and reference Table I. Stop sampling if the cumulative total is outside of the continue sampling bands and classify populations appropriately. If the cumulative total remains within 

the continue sampling band that delineates the light and moderate population levels after 

sampling 300 trees, classify populations as light-moderate. If the count remains within the 

continue sampling band that delineates the moderate and severe population levels after counting 2,000 galls, classify populations as moderate-severe.

Note

This plan was not validated with data from other areas of Canada or the USA and should be 

used with caution until it has been validated for other regions. Although sequential sampling 

can substantially save time and effort, the plan outlined here quickly becomes labor intensive 

when M. piceae populations fall between the light to moderate ranges.

References

Felt, E. P. 1926. A new spruce gall midge (Itonidae). Canadian Entomologist 58: 229-230.

Smith, C. C. 1952. The life-history and galls of a spruce gall midge, Phytophaga piceae Felt 

(Diptera: Cecidomyiidae). Canadian Entomologist 84: 272-275.

Table

Table I. Decision values of the M. piceae sequential sampling plan. Counts are the accumulation of the average number of galls pooled from a four-branch sample per tree.

Number of 

trees sampled

<Value = 

light population

In between values= moderate population>Value= severe 

population

5 -

Continue Sampling

- -

Continue Sampling

468

10 - - - 568

15 - - - 669

20 - - - 769

25 - - - 870

30 8 202 263 970

35 26 220 337 1071

40 43 237 437 1171

45 61 255 538 1272

50 78 272 638 1372

55 96 290 739 1473

60 113 307 839 1573

65 131 325 940 1674

70 148 342 1040 1774
18

75 166

Continue Sampling

360 1141

Continue Sampling

1875

80 183 377 1241 1975

85 201 395 1342 2076

90 218 412 1442 2176

95 236 430 1543 2277

100 253 447 1643 2377

105 271 465 1744 2478

110 288 482 1844 2578

115 306 500 1945 2679

120 323 517 2045 2779

125 341 535 2146 2880

130 358 552 2246 2980

135 376 570 2347 3081

140 393 587 2447 3181

145 411 605 2548 3282

150 428 622 2648 3382

155 446 640 2749 3483

160 463 657 2849 3583

165 481 675 2950 3684

170 498 692 3050 3784

175 516 710 3151 3885

180 533 727 3251 3985

185 551 745 3352 4086

190 568 762 3452 4186

195 586 780 3553 4287

200 603 797 3653 4387

205 621 815 3754 4488

210 638 832 3854 4588

215 656 850 3955 4689

220 673 867 4055 4789

225 691 885 4156 4890

230 708 902 4256 4990

235 726 920 4357 5091

240 743 937 4457 5191

245 761 955 4558 5292

250 778 972 4658 5392

255 796 990 4759 5493

260 813 1007 4859 5593
19

265 831

Continue Sampling

1025 4960

Continue Sampling

5694

270 848 1042 5060 5794

275 866 1060 5161 5895

280 883 1077 5261 5995

285 901 1095 5362 6096

290 918 1112 5462 6196

295 936 1130 5563 6297

300 953 1147 5663 6397

 Table generated from equations listed in Figure 2 of the original publication.
20

Jeffrey Pine Needle Miner

Coleotechnites sp.

Lepidoptera: Gelechiidae

Unruh, T. R.; Luck, R. F. 1982. Comparison of two sampling methods for the Jeffrey pine 

needle miner, Coleotechnites sp. (Lepidoptera: Gelechiidae) in southern California. Canadian Entomologist 114: 605-615.

Objective

To propose a sampling plan for Coleotechnites sp.

Abstract

Jeffrey pine needle miner, Coleotechnites sp., is an aesthetic pest and stressor of Jeffrey pine, 

Pinus jeffreyi Grev. and Balf., in California. The caterpillars feed inside all needles older 

than the current year. Sporadic outbreaks may occur with severe defoliation and growth 

reduction. Often very little mortality is caused by this pest but secondary pests, such as the 

Jeffrey pine beetle, Dendroctonus jeffreyi Hopkins, and the California flatheaded borer, Melanophia californica Van Dyke, can attack and kill trees stressed by Jeffrey pine needle miner. 

No information was previously available on intra- and inter-tree distribution of Jeffrey pine 

needle miner, therefore two sampling plans were developed for this pest. One plan was 

developed from the component of variance technique (Cochran 1963; Southwood 1978) and 

the other from the mean crowding relationship (Kuno 1976). The mean crowding technique 

was recommended over the component of variance technique because it requires no assumptions about the population distribution of the insect and because it also is easily adapted to 

sequential sampling techniques. Sampling 90 tips (1 tip per tree) or 30 tips (6 tips per tree) 

was found to give good estimates of population density at a site at the 10% level of precision. 

For integrated pest management of Coleotechnites sp., additional research is needed to relate 

population estimates to resulting levels of defoliation by this pest. 

Sampling Procedure

The upper crown of P. jeffreyi contained more currently mined needles than either the middle 

or lower crown. The majority of current mines were found in fully-grown needles ≤4 years 

old. Refer to Table III to determine the number of trees to sample based on the number of 

tips sampled per tree. Randomly select 15- to 40-cm branch tips from the upper third of the 

live crown of P. jeffreyi. Trees should be between 6–10 m tall. Count the number of needles 

with current mines containing live Coleotechnites sp. larvae, pupae, or parasitoids. Average 

counts among trees if sampling 1 tip per tree. Average counts among tips if sampling >1 tip 
21

per tree and then average the estimates among trees.

Notes

The 10% level of precision is usually intended for detailed studies where large sample sizes 

are required. The 25% level of precision is more appropriate for management purposes, and 

can be calculated by the following equation: 

where N = the number of trees required for a given value of precsion, n = sample size of tips 

per tree, m = mean, a = intercept, b1

 = the slope of the regression of mean on mean crowding 

for the composite population, b2

 = the slope of the mean on mean crowding for trees, and D2

0

= (Var m)/m2 (Kuno 1976). Change the value of the variable D2

0 from 0.1 to 0.25, or any 

other desired level of precision. The other variables in this equation can be taken from Table 

III. See the original publication for additional information. 

To be applicable over a larger geographic area and with larger and/or smaller trees, sampling 

additional stands/trees is necessary. Consequently, please use this plan with caution.

References

Cochran, W. G. 1963. Sampling techniques, 2nd ed. New York: John Wiley and Sons, 413 p.

Kuno, E. 1976. Multistage sampling for population estimation. Researches on Population 

Ecology 18: 39-56.

Southwood, T. R. E. 1978. Ecological methods with particular reference to the study of insect 

populations, 2nd ed. London: Chapman and Hall; 524 p

 

N = 1

n

α +1

m

+ β1(β 2 −1)

β 2

 

  

 

  + β1 − β 2

β 2

 

  

 

  D0

2
22Table 

Table III. sizes necessary (number of trees at a rate of n tips per tree) to estimate the mean density of needles infested with the Jeffrey pine needle miner with 10% precision along with the parameters required for the mean crowding technique. Parameters for the mean crowding technique are derived from each plot (A), and from the plots pooled (B)._________________________________________________________________________________________________________

 n=

Plot Mean

Mean 

crowding

(m*)

Intercept

(α)

β1 β2 r2 1 2 3 6 15 30

A

Raw Snow 

Valley

32.8 53.2 5.28 1.45 1.13 0.957 65 47 41 35 31 30

Erwin 

Lake

29.4 57.4 -0.18 1.96 1.62 0.846 99 60 47 34 27 24

Big Bear 

City

50.9 84.8 8.60 1.50 1.18 0.940 69 48 41 34 30 28

B

Raw Snow 

Valley

32.8 - 6.26 1.512 1.2302 0.86 74 49 41 32 27 25

Erwin 

Lake

29.4 - 6.26 1.512 1.2302 0.86 77 50 41 33 26 24

Big Bear 

City

50.9 - 6.26 1.512 1.2302 0.86 67 45 38 31 27 25

Table III modified and reproduced with permission from the authors, granted April 2, 2009.
23

European Pine Shoot Moth

Rhyacionia buoliana (Denis & Schiffermüller)

Lepidoptera: Tortricidae

Gray, T. G.; Slessor, K. N.; Shepherd, R. F.; Grant, G. G.; Manville, J. F. 1984. European 

pine shoot moth, Rhyacionia buoliana (Lepidoptera: Tortricidae): identification of additional pheromone components resulting in an improved lure. Canadian Entomologist 116: 

1525–1532. 

Objective

To improve the pheromone lure for R. buoliana through the addition of other components. 

Abstract

European pine shoot moth, Rhyacionia buoliana (Denis & Schiff.), has become an important 

pest of two- and three-needle pines (Pinus spp.) in the Pacific Northwest, the Lake States, 

the upper Midwest, and northeastern USA, as well as in southern Canada. Red pine (Pinus 

resinosa Ait.) is heavily damaged by R. buoliana, but other susceptible hosts include Scotch 

pine (Pinus sylvestris L.), lodgepole pine (Pinus contorta Dougl.), and ponderosa pine (Pinus ponderosa Laws.) This insect primarily infests terminal shoots causing severe deformation and reduced growth. 

Pheromone trapping is an important tool in monitoring populations of R. buoliana. The sex 

pheromone of R. buoliana was identified as E-9-dodecenyl acetate, but commercial preparations of the lure did not attract the expected numbers of males in the field. Additional 

research has identified a 97:3 ratio of E-9-dodecenyl acetate:E-9-dodecenol as a stronger 

lure than either virgin females or E-9-dodecenyl acetate alone. Dodecanol (0.03% or more) 

inhibited male moths in the absence of dodecyl acetate but had no effect when both E-9-

dodecenyl acetate and E-9-dodecenol were present. Dodecyl acetate masked this inhibition 

when present at concentrations of 0.3% or greater.

Lures embedded in polyvinyl chloride rods performed better than lures loaded in rubber 

septa, possibly due to varying release rates from the septa. In contrast, pheromone release 

from the rods was constant.

Sampling Procedure

Construct triangular traps from 2-liter milk cartons so that the interior surface measures 695 

cm2

. Coat the interior surface with an adhesive material. Insert in each trap a 2.5 x 3 mm 

diameter lure of polyvinyl chloride rod (5% by weight) containing a 97:3 ratio of E-9-dode-
24

cenyl acetate:E-9-dodecenol embedded separately. Hang traps on Scotch pines 2–4 m above 

ground and 20 m apart. Orient traps with one edge directed to the ground. Leave traps in 

place 4–9 days before servicing.
25

European Pine Shoot Moth

Rhyacionia buoliana (Denis & Schiffermüller)

Lepidoptera: Tortricidae

Regan, R. P.; De Angelis, J. D.; Gredler, G. 1991. Predicting seasonal flight of European pine 

shoot moth (Lepidoptera: Tortricidae) in western Oregon. Environmental Entomology 20: 

1403–1406. 

Objective

To predict seasonal flight of R. buoliana in Oregon using degree-day accumulations. 

Abstract

European pine shoot moth, Rhyacionia buoliana (Denis & Schiff.), has become an important pest of two- and three-needle pines (Pinus spp.) in ornamental settings in the Pacific 

Northwest, the Lake States, the upper Midwest, and northeastern USA, as well as in southern 

Canada.

Red pine (Pinus resinosa Ait.) is heavily damaged by R. buoliana, but other susceptible hosts 

include Scotch pine (Pinus sylvestris L.), lodgepole pine (Pinus contorta Dougl.), and ponderosa pine (Pinus ponderosa Laws.) This insect primarily infests terminal shoots causing 

severe deformation and reduced growth. 

Currently, chemical control of R. buoliana is directed towards the adults in early summer, 

preferably before mating occurs, or against newly hatched larvae. Adult activity can be 

predicted by estimating pupal densities, with first flight occurring when populations consist 

of approximately 20% larvae and 80% pupae (Antonelli and Campbell 1985), or based on 

phenological observations of needle development on host trees. However, degree-day accumulation offers a less time-consuming and less laborious method of predicting adult activity 

than population sampling. Likewise, it may be more practical for nurseries growing many 

different cultivars with differing phenologies. 

Cumulative trap catch of adult male R. buoliana (Y) was related positively to degree-day 

accumulation above a base temperature of -2.2 ̊C (X) (Y = -267.8 + 0.162X; r2

 = 0.965; P > 

0.001). Insecticide applications could be timed to coincide with adult activity, as predicted 

by degree-day accumulations. However, optimal timing of insecticide application against 

adult R. buoliana remains to be developed, as populations in western Oregon are currently 

insufficient for testing. 
26

Sampling Procedure

Monitor emergence of male R. buoliana using Pherocon II pheromone traps (Trécé, Inc., 

Salinas, CA). Bait traps using a 97:3 blend of E-9-dodecenyl acetate and E-9-dodecenyl 

alcohol (1% active ingredient) embedded as a controlled-release formulation in polyvinylchloride. Hang traps in the canopies of Scotch pine, lodgepole pine, or mugo pine at 1.5–2 m 

above ground. Space traps at least 25 m apart. Check traps three times a week beginning the 

second week of May until the end of July. Replace lures 3–4 times during the flight period. 

Starting January 1, monitor degree-day accumulations above the base temperature of -2.2 ̊C. 

Use the following model to predict the cumulative male moth trap catch:

Y = -267.8 + 0.162X

where Y = percent cumulative trap catch of male R. buoliana between 5 and 95% and X = 

number of degree-days above -2.2 ̊C. Reference the table below for the predicted degree-day 

accumulations for 10, 50, and 90% trap catch. 

% Cumulative trap catch Accumulated degree-days

10% 1,712

50% 1,958

90% 2,205

Land managers can time insecticide applications to coincide with adult activity as predicted 

by degree-day accumulations. However, optimal timing of insecticide application against 

adult R. buoliana has not been determined.

 

Notes

This model was developed in western Oregon and should be used with caution in other regions. Insufficient densities of R. buoliana occur in western Oregon to determine the optimal 

timing of chemical control for this pest.

Reference

Antonelli, A. L.; Campbell, R. L. 1985. European pine shoot moth. Washington State University College of Agriculture Extension Bulletin 1011.
27

Nantucket Pine Tip Moth

Rhyacionia frustrana (Comstock)

Lepidoptera: Tortricidae

Malinoski, M. K.; Paine, T. D. 1988. A degree-day model to predict Nantucket pine tip moth, 

Rhyacionia frustrana (Comstock) (Lepidoptera: Tortricidae), flights in southern California. 

Environmental Entomology 17: 75–79.

Objective

To develop a degree-day model for R. frustrana flight periods in order to improve the timing 

of treatment applications in southern California.

Abstract

Nantucket pine tip moth, Rhyacionia frustrana (Comstock), is a common pest of young 

loblolly, Pinus taeda L., shortleaf, P. echinata Mill., and Virginia, P. virginiana Mill., pine 

plantations in the eastern USA. In southern California it prefers Monterey pine, P. radiata D. 

Don, which is grown as Christmas trees. Larval feeding can cause shoot mortality and tree 

deformity, reductions in height and volume growth, and occasional tree mortality. Young 

larvae are very small and can be difficult to sample on the foliage. Larvae tunnel into shoots, 

where they are protected from insecticide sprays, before shoot injury becomes apparent. The 

aesthetic value of pines grown as Christmas trees is high enough that current control practices for R. frustrana include preventive sprays based on calendar date.

A degree-day model (oC) based on field data was developed to predict when early-instar R. 

frustrana would be feeding on Monterey pine foliage in Christmas tree plantations in southern California. This is when insecticide sprays are most effective in controlling R. frustrana. 

The model predicts that a mean total of 575 degree-days (575DD) accumulate between the 

capture of the first male in pheromone-baited traps and peak flight period. Lower and upper 

thresholds of 5.50o and 37.25oC, respectively, are incorporated in the model to account for 

when development would cease due to lower and higher temperatures in California. Early 

instars are found feeding on foliage at 111DD after peak flight period, or at 686DD after the 

first moth is found in the pheromone-baited trap. Insecticide applications are most effective 

when applied at this time. Pheromone traps and degree-day monitoring should be conducted 

for each of the four flight periods of R. frustrana in southern California. Compared to current control practices for R. frustrana, using this model should reduce the number of insecticide sprays applied and identify when larvae are the most vulnerable to chemical control.
28

Sampling Procedure

Install delta traps baited with R. frustrana pheromone lures in early January. Hang traps on 

the outer canopy of Monterey pines 1.5–1.8 m above ground. Inspect traps daily for moth 

capture. Replace lures every 4 weeks. Four generations of R. frustrana occur in southern 

California and traps should be deployed prior to the start of each flight period. Replace traps 

as needed throughout the year. 

Begin monitoring degree-day (oC) accumulation at the start of each flight period, as indicated 

by when the first moth is found in the pheromone-baited traps. Do not include degree-days 

above the upper threshold of 37.25oC or below the lower threshold of 5.50oC. Expect peak 

flight to occur at 575DD after the capture of the first moth in the traps. Apply treatments 

against first instars 111DD after peak flight, or 686DD after the first moth is captured in the 

pheromone trap.
29

Nantucket Pine Tip Moth

Rhyacionia frustrana (Comstock)

Lepidoptera: Tortricidae

Asaro, C.; Cameron, R. S.; Nowak, J. T.; Grosman, D. M.; Seckinger, J. O.; Berisford, C. W. 

2004. Efficacy of wing versus delta traps for predicting infestation levels of four generations of the Nantucket pine tip moth in the southern United States. Environmental Entomology 33: 397-404.

Objective

To compare two different types of pheromone traps for efficient capture of R. frustrana and 

validate earlier models (Asaro and Berisford 2001) for predicting damage levels.

Abstract

The Nantucket pine tip moth, Rhyacionia frustrana (Comstock), is a common pest of young 

loblolly, Pinus taeda L., shortleaf, P. echinata Mill., and Virginia, P. virginiana Mill., pine 

plantations in the eastern USA. Larval feeding can cause shoot mortality and tree deformity, 

reductions in height and volume growth, increases in compression wood formation, and occasional tree mortality. 

Two trap types (wing and delta traps) were evaluated in loblolly pine plantations in four 

southern states for efficacy in catching male R. frustrana. Wing traps were more useful at 

predicting tip moth density and tree damage than delta traps, especially for the first three tip 

moth generations (r2

 = 0.46 to 0.65). Average top whorl damage was related positively to 

average cumulative wing trap catch of the first (y = 10.026 + 0.030x; r2 = 0.53), second (y 

= 12.784 + 0.062x; r2 = 0.44) and third (y = 11.747 + 0.217x; r2 = 0.62) generations. This 

trapping strategy validates the findings of Asaro and Berisford (2001) (see our review in this 

volume), who found that R. frustrana population density and percentage infested shoots was 

highly correlated with subsequent trap catch in the Georgia Piedmont. As such, the trapping 

strategy presented here should be useful for timing insecticide treatments when needed.

Sampling Procedure

Hang a Pherocon 1C wing trap (Trécé, Salinas, CA) 1–1.5 m high in the top whorl of four 

loblolly trees, or at the same height on steel conduit posts if trees are too short. Traps should 

be hung throughout the plantation, at least 30 m inside of the edge of the plantation and 60 m 

apart. Bait traps with rubber septa loaded with the two-component R. frustrana pheromone 

(Hill et al. 1981; Asaro et al. 2001). Replace baits monthly from January to mid-March/early 

April, the flight period of the first generation. Baits do not require replacement in subsequent 
30

flight periods, which begin in May, June-July, and August. Traps should remain in place until 

the predicted spray date of the following generation. Refer to Fig. 3 to relate the average cumulative trap catch before the spray date for the first adult generation to the average percentage of shoots in the top whorl infested by the first brood generation. 

Notes

The thresholds presented in Fig. 3 for low, moderate, and heavy damage levels are hypothetical and based on the experience and judgment of the authors. No damage categories have 

been established for R. frustrana, therefore use the levels presented here with caution.

References

Asaro, C.; Berisford, C. W. 2001. Predicting infestation levels of the Nantucket pine tip 

moth (Lepidoptera: Tortricidae) using pheromone traps. Environmental Entomology 30: 

776-784.

Asaro, C.; Dalusky, M. J.; Berisford, C. W. 2001. Quantity and ratio of pheromone components among multiple generations of the Nantucket pine tip moth (Lepidoptera: Tortricidae) in Georgia and Virginia. Environmental Entomology 30: 1006-1011.

Hill, A. S.; Berisford, C. W.; Brady, U. E.; Roelofs, W. L. 1981. Nantucket pine tip moth, 

Rhyacionia frustrana: identification of two sex pheromone components. Journal of 

Chemical Ecology 7: 517-528.
31

Fig 3. Diagrammatic illustration of how to use the wing trap regression model A to forecast 

subsequent damage levels (first generation brood) and make a decision regarding insecticide 

application. The cut-off values delineating low, moderate, and heavy damage are hypothetical and based on the experience and judgment of the authors.

Figure 3 modified and reprinted with permission from Environmental Entomology, 

April 2, 2009.
32

Nantucket Pine Tip Moth

Rhyacionia frustrana (Comstock)

Lepidoptera: Tortricidae

Asaro, C.; Berisford, C. W. 2001. Predicting infestation levels of the Nantucket pine tip moth 

(Lepidoptera: Tortricidae) using pheromone traps. Environmental Entomology 30: 776-

784.

Objective

To correlate trap catches of R. frustrana with population density and host damage in the 

Georgia Piedmont.

Abstract

Nantucket pine tip moth, Rhyacionia frustrana (Comstock), is a common pest of young 

loblolly, Pinus taeda L., shortleaf, P. echinata Mill., and Virginia, P. virginiana Mill., pine 

plantations in the eastern USA. Larval feeding can cause shoot mortality and tree deformity, 

reductions in height and volume growth, increases in compression wood formation, and occasional tree mortality. Research was conducted in the Georgia Piedmont over one to several 

years to correlate trap catch with population density and tree damage.

Hyperbolic curves best described the relationships between total trap catch and moth population density, and between trap catch and shoot damage, for one generation and the next. 

These relationships suggest that trap saturation may occur at very high densities of adult 

moths. However, relationships between trap catch of one generation to the density of subsequent generations and their damage had predictive values. Total trap catch of the first adult 

generation of R. frustrana was related positively to shoot damage produced by first generation brood (y = 0.024x – 2.022; r2

 = 0.87). Trap catch of the first generation was related 

positively to the density of the overwintering brood generation (y = 2815.277x /(2.014 + x); 

r2

 = 0.89). Likewise, trap catch of the first generation was related positively to shoot damage caused by the overwintering brood generation (y = 4660.129x /(61.730 + x); r2

 = 0.82). 

However, trap catch of the second generation gave only fair predictions of later population 

densities or host damage.

Sampling Procedure

Use Pherocon 1C wing traps (Trécé, Salinas, CA) with rubber septa loaded with R. frustrana

pheromone. Select a 2-ha rectangular plot within a plantation. Set traps with one in each 

corner of the plot and one halfway along both of the two longest sides for a total of 6 traps 

per plot. Traps should be at least 30 m inside the edge of the plantation and apart from each 
33

other. Replace baits weekly for maximum efficacy. Record the number of trapped males and 

empty traps or replace the bottoms every 3-5 days. Assume that a certain percentage of the 

total moths for a given emergence period will be trapped before the spray date. Spray dates 

for much of the southeastern USA can be predicted using degree-day accumulations (Fettig et 

al. 2000).

Note

The authors recommend use of this procedure with caution, as trap efficacy might be regionspecific and validation may be required before widespread use is considered. While no 

damage threshold has been established for R. frustrana, the authors suggest that control is 

warranted when the cumulative trap catch within a plot exceeds 1,000 moths before the spray 

date, given that <40% infested shoots appears to limit host growth.

Reference

Fettig, C. J.; Dalusky, M. J.; Berisford, C. W. 2000. Nantucket pine tip moth phenology and 

timing of insecticide spray applications in seven southeastern states. Res. Pap. SRS-18. 

Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station; 23 p. 
34
35

DEFOLIATING INSECTS
36
37

Cottonwood Leaf Beetle

Chrysomela scripta F.

Coleoptera: Chrysomelidae

Fang, Y.; Hart, E. R. 2000. Effect of cottonwood leaf beetle (Coleoptera: Chrysomelidae) larval population levels on Populus terminal damage. Environmental Entomology 29: 43-48.

Objective

To relate density of C. scripta egg masses to defoliation levels on a hybrid poplar using a 

damage rating system.

Abstract

Cottonwood leaf beetle, Chrysomela scripta Fabricius, is an important pest of poplar and aspen, Populus spp., and willow, Salix spp., in North America. Larvae prefer to feed on young 

leaves and can produce significant defoliation. The second generation of C. scripta has fewer 

natural enemies and is less susceptible to abiotic factors, therefore it is more damaging than 

the first or third generations. Populus grown in plantations as a short-rotation woody crop is 

particularly susceptible to damage by C. scripta during the first 3 years when the trees produce abundant new growth. Reichenbacker et al. (1996) reported that approximately 75% 

defoliation in Populus reduced the above ground, root, and total biomass by approximately 

33% after 2 years of defoliation.

A study was conducted on the relationship between larval density and resulting defoliation 

on the hybrid popular Populus deltoides Marsh. x Populus nigra L. var. ‘Eugenei’in Iowa, 

U.S.A. For second-generation C. scripta, there was a significant relationship between the 

egg mass equivalent per actively growing terminal and the resulting defoliation on open trees 

(R2

 = 0.8714; P = 0.0001), as expressed by the equation y = –0.91x2 + 3.79x + 0.61, where y 

= damage rating and x = egg mass equivalent per terminal. An egg mass equivalent was defined as an egg mass or a newly eclosed larval clutch. When the density of egg mass equivalents was >1 per leaf terminal, the expected level of defoliation was >75%. However, the 

relationship between beetle densities of <1 egg mass equivalent per terminal and expected 

level of defoliation was not as well defined. 

Based on these data, the authors concluded that defoliation by C. scripta should reduce 

biomass by <33% over the first two years of plantation establishment as egg mass densities 

increase from 0 to 1 per terminal in each generation. In addition, the damage rating standard 

developed in this study offers a quick and reliable method of determining the defoliation 

level present in a stand comparable to measuring percent defoliation. 

Sampling Procedure

Randomly sample actively growing leaf terminals of hybrid Populus spp. during peak oviposition by the second generation of C. scripta, in late June-early July in Iowa. An actively 
38

growing terminal has new growth with leaves in good health and a leaf plastochron index 

(LPI) of 0–8. Briefly, designate the terminal, expanding leaf with a blade length nearest to 

3 cm as LPI = 0. Designate the next leaf counted consecutively down the stem from apex to 

base as LPI = 1. Continue to number the leaves in this manner until a total of 8 leaves have 

been selected (as described in Fang et al. 2002). Examine each of these 8 leaves in the terminal and calculate the mean egg mass or newly eclosed larval clutch per terminal, referred to 

as the egg mass equivalent by the authors. Use the following equation to predict the expected 

damage rating from second-generation C. scripta:

y = –0.91x2

 + 3.79x + 0.61

where y = damage rating and x = egg mass equivalent per terminal. Relate the expected 

damaging rating to the leaf plastochron index using the following table:

Damage 

Rating Damage on Leaf Plastochron Index (LPI) 

0 No feeding on LPI 0-8

1 Light feeding, sample feeding only on LPI 0–8; 

<33% of LPI 0–8 consumed

2 Light to moderate feeding; 33–50% of LPI 0–8 consumed

3 Moderate to heavy feeding; approx. 50–75% of LPI 

0–8 consumed; main leader intact

4 Heavy feeding; >75% of LPI 0–8 consumed; main 

leader and terminal bud heavily damaged

In general, consider treating for C. scripta when the density of egg masses or larval clutches 

is >1 per leaf terminal, as the expected level of defoliation associated with this density is 

>75%.

Notes

The authors did not specify how many trees or how many terminals per tree should be sampled. Managers may want to review the economic injury level determined for second-generation C. scripta on 2-year old Populus deltoides Marsh. X Populus nigra L. var. ‘Eugenei’ 

(Fang et al. 2002), also included in this volume.

References

# Fang, Y.; Pedigo, L. P.; Colletti, J. P.; Hart, E. R. 2002. Economic injury level for second-generation 

cottonwood leaf beetle (Coleoptera: Chrysomelidae) in two-year-old Populus. Journal of Economic 

Entomology 95: 313-316.

Reichenbacker, R. R.; Schultz, R. C.; Hart, E. R. 1996. Artificial defoliation effect on Populus growth, 

biomass production, and total nonstructural carbohydrate concentration. Environmental Entomology 25: 632-642. 
39

Cottonwood Leaf Beetle

Chrysomela scripta F.

Coleoptera: Chrysomelidae

Fang, Y.; Pedigo, L. P.; Colletti, J. P.; Hart, E. R. 2002. Economic injury level for secondgeneration cottonwood leaf beetle (Coleoptera: Chrysomelidae) in two-year-old Populus. 

Journal of Economic Entomology 95: 313-316.

Objective

To develop an economic injury level (EIL) for the poplar hybrid, Populus deltoides Bartr. ex. 

Marsh x Populus nigra L. (Populus var. ‘Eugenei’), based on the relationship between the 

density of C. scripta egg masses and subsequent defoliation by larvae.

Abstract

Cottonwood leaf beetle, Chrysomela scripta Fabricius, is an important pest of poplar in North 

America. Larvae prefer to feed on young cottonwood leaves, causing significant defoliation 

in the absence of insecticide use. The second generation of C. scripta has fewer natural enemies and is less susceptible to abiotic mortality factors, therefore it is more damaging than 

the first or third generations. Populus grown in plantations as a short-rotation woody crop 

is particularly susceptible to damage by C. scripta during the first 3 years, when the trees 

produce abundant new growth. Reichenbacker et al. (1996) reported that defoliation levels 

approaching 75% of Leaf Plastochron Index 0–8 of actively growing terminals in 2-year-old 

Populus reduced the above ground, root, and total biomass by approximately 33%. 

Fang and Hart (2000) found that increasing numbers of egg masses per terminal shoot (X) 

was related positively to the percent defoliation (Y) (Y = 68.13X + 6.31, r2

 = 0.89) produced 

by second-generation C. scripta attacking 2-year-old poplars. This equation, along with the 

findings of Reichenbacker et al. (1996) and calculations of market value and management 

costs per hectare, was used to develop EIL values for second-generation C. scripta on hybrid 

poplar in central Iowa. Resulting EIL values varied from 0.2 to 0.9 egg masses per actively 

growing terminal shoot, depending on market value and management costs per hectare. Prior 

to this publication, no EIL existed for C. scripta and managers applied pesticides whenever 

they felt populations required control measures.

Sampling Procedure

Determine the market value per kg of the crop. The market value of Populus may differ 

depending on if the crop is grown for pulp or fuel biomass. Determine the management costs 

of the crop per hectare (cost of insecticide and the application method used). Use Table 1 to 
40

determine the number of egg masses per actively growing terminal representing the EIL for 

second-generation C. scripta with regard to market value and management costs. 

During peak oviposition by first generation females (late June-early July in central Iowa), 

randomly select 30 trees with actively growing terminals (Fang and Hart 2000). Actively 

growing terminals should have healthy new growth classified as 0¬–8 on the Leaf Plastrochron Index (LPI). For Populus spp., designate the terminal, expanding leaf with a blade 

length nearest to 3 cm as LPI = 0. Designate the next leaf counted consecutively down the 

stem from apex to base as LPI = 1. Continue to number the leaves in this manner until a total 

of 8 progressively older leaves have been selected. Determine the mean number of secondgeneration C. scripta egg masses per actively growing terminal (Fang and Hart 2000; see our 

review in this volume). Control measures are warranted if the average egg mass density per 

actively growing terminal exceeds the chosen EIL value from Table 1.

Table

Table 1. Economic injury level (egg masses per actively growing terminal) for the cottonwood leaf beetle, second generation, in 2-yr-old Populus var. Eugenei.

Market 

Value, $/kg

Management cost $/ha

25 26 27 28 29 30

0.02 0.8 0.8 0.8 0.9 0.9 0.9

0.03 0.5 0.5 0.6 0.6 0.6 0.6

0.04 0.4 0.4 0.4 0.4 0.5 0.5

0.05 0.3 0.3 0.3 0.4 0.4 0.4

0.06 0.3 0.3 0.3 0.3 0.3 0.3

0.07 0.2 0.2 0.3 0.3 0.3 0.3

Table 1 reproduced with permission from the Journal of Economic Entomology, granted April 2, 2009.

Notes

The economic threshold was set as equal to the EIL in this study, therefore control measures 

should be applied as soon as the average egg mass density per actively growing terminal 

exceeds the chosen EIL value. The EIL values listed here were determined specifically for 

2-year old Populus var. ‘Eugenei’ grown in Iowa. These EIL values may not be suitable for 

different species or hybrids of Populus, for different age classes, or for Populus var. ‘Eugenei’ grown in other regions. Further research is needed to determine EIL values for older 

stands of this hybrid and for other generations of C. scripta. Close monitoring of the C. 

scripta population is needed to ensure that only the second generation is sampled, and not the 

first or third generations.
41

References

# Fang, Y.; Hart, E. R. 2000. Effect of cottonwood leaf beetle (Coleoptera: Chrysomelidae) 

larval population levels on Populus terminal damage. Environmental Entomology 29: 43-

46.

Reichenbacker, R. R.; Schultz, R. C.; Hart, E. R. 1996. Artificial defoliation effect on Populus growth, biomass production, and total nonstructural carbohydrate concentration. Environmental Entomology 25: 632-642.
42

Elm Leaf Beetle

Pyrrhalta (=Xanthogaleruca) luteola (Müller)

Coleoptera: Chrysomelidae

Dahlsten, D. L.; Rowney, D. L.; Tait, S. M. 1994. Development of integrated pest management programs in urban forests: the elm leaf beetle (Xanthogaleruca luteola (Müller)) in 

California, USA. Forest Ecology and Management 65: 31-44.

Objective

To develop a pest management program for P. luteola in stands of urban elms based on 

degree-day accumulation above 11°C.

Abstract

Elm leaf beetle, Pyrrhalta (=Xanthogaleruca) luteola (Müller), is one of the most important 

pests of urban elms, Ulmus spp., in the U.S. and Canada. Larvae injure the host tree by skeletonizing leaves. 

An integrated pest management program for P. luteola, based on degree-day (DD) accumulation above 11°C, was developed for urban English elms (Ulmus procera Salisb.) in northern 

and central California. The authors were unable to develop a model predicting defoliation 

damage at the end of the season based on peak egg densities present in the spring. Each 

generation of P. luteola varies in its potential to defoliate elms and should be monitored 

separately. Egg masses on English elms should be sampled around 275DD and 870DD for 

the first and second generations, respectively. Presence-absence sampling of egg masses on 

30-cm branch terminals is sufficient to determine if populations of P. luteola warrant control 

measures. If the proportion of samples with first generation egg masses is >0.45, then the expected damage should be >40% and in the acceptable range with an error probability of 10%. 

If the proportion of samples with second generation egg is >0.30, then the expected damage 

should be >40% and in the acceptable range with an error probability of 10%. The damage 

threshold of 40% was based on experience with homeowners and tree managers. Suggested 

sample sizes for differently sized stands are presented, and foliar treatment, if warranted, can 

be based on degree-day accumulation as well. If control is warranted, apply treatments when 

early instar larvae peak, around 350DD (base 11°C) for the first generation and 1,100DD 

(base 11°C) for the second generation. While management decisions for P. luteola can be 

timed using degree-day accumulation, elms should still be sampled to confirm the presence 

of this pest before treatment.
43

Sampling Procedure

Beginning March 1, monitor degree-day accumulation above the threshold temperature of 

11 ̊C. Adult P. luteola activity peaks at around 250DD. Sample P. luteola egg masses on 

elms around 275DD and 870DD for first and second generations, respectively. Refer to 

Table 3 to determine the appropriate number of trees to sample within each stand, based on 

the total number of trees within a stand. Randomly sample 30-cm branch terminals from the 

lower crown of each tree in all four cardinal directions. Examine each branch terminal for 

the presence of egg masses and tally the number of terminals with egg masses present. If the 

proportion of samples with first generation egg masses present is >0.45, then the expected 

damage should be >40% and in the acceptable range with an error probability of 10%. If the 

proportion of samples with second generation egg masses present is >0.30, then the expected 

damage should be >40% and in the acceptable range with an error probability of 10%. 

Consider applying foliar treatments if the proportion of samples with egg masses exceeds 

0.45 or 0.30 for the first and second generation of P. luteola, respectively. If control is warranted, apply treatments when early instar larvae peak, around 350DD (base 11°C) for the 

first generation and 1,100DD (base 11°C) for the second generation.

Notes

This research was conducted in northern and central California and the degree-day accumulation correlated to oviposition and peak density of young larvae may not be valid in other 

areas. Use these recommendations with caution until validated in other regions. 
44

Table

Table 3. Suggested sample size for elm leaf beetle egg clusters on English elm in different 

size standsa

Total trees in 

stand Trees Sampled

Samples 

per tree

Samples 

per segment Total samples

Percentage of 

trees sampled

3 3 40 5 120 100

4 4 32 4 128 100

5 5 32 4 160 100

6 6 24 3 144 100

7 6 24 3 144 86

8 7 24 3 168 88

9 8 16 2 128 89

10 8 16 2 128 80

11 8 16 2 128 73

12 8 16 2 128 67

13 8 16 2 128 62

14 8 16 2 128 57

15 8 16 2 128 53

16 9 16 2 144 56

17 9 16 2 144 53

18 9 16 2 144 50

19 9 16 2 144 47

20 9 16 2 144 45

21 9 16 2 144 43

22 10 16 2 160 45

23 10 16 2 160 43

24 10 16 2 160 42

25 10 16 2 160 40

26 10 16 2 160 38

27 10 16 2 160 37

28 10 16 2 160 36

29 10 16 2 160 34

30 10 16 2 160 33

40 12 16 2 192 30

50 15 16 2 240 30

60 15 16 2 240 25

a

Criteria: (i) minimum of 128 branches; (ii) minimum of 25% of trees sampled; (iii) eight trees or more sampled 

if possible.

Table 3 reprinted with permission from Elsevier Limited, granted July 9, 2009.
45

Elm Leaf Beetle

Pyrrhalta (=Xanthogaleruca) luteola (Müller)

Coleoptera: Chrysomelidae

Dreistadt, S. H.; Dahlsten, D. L. 1989. Density-damage relationship and presence-absence 

sampling of elm leaf beetle (Coleoptera: Chrysomelidae) in northern California. Environmental Entomology 18: 849-853.

Objective

To develop a presence-absence sampling method for P. luteola in order to predict late season 

defoliation based on early season beetle density.

Abstract

The elm leaf beetle, Pyrrhalta (=Xanthogaleruca) luteola (Müller), is one of the most important pests of urban elms, Ulmus spp., in the U.S. and Canada. Larvae injure the host tree by 

skeletonizing leaves. A presence-absence sampling method was developed that could forecast late season defoliation of English elm, Ulmus procera Salisb., from the density of first 

generation eggs.

The proportion of 30 cm-long branch terminals (X) infested with at least one P. luteola

egg was related positively to cumulative apparent damage (Y) of English elm (Y = 0.97 + 

17.42X; r2 = 0.86). The number of 30 cm-long branch terminals needed to estimate the proportion of terminals infested with at least one P. luteola egg at the 0.3 and 0.5 levels of fixed 

precision is given in Fig. 2. If the actual infestation level is 40% of terminals infested with 

at least one egg, then 20 and 4 branch terminals need to be sampled to meet the 0.3 and 0.5 

levels of fixed precision, respectively.

Sampling Procedure

During the oviposition period of the first generation, randomly select 30-cm branch terminals 

from the lower third of the canopy on English elms. Take five inner and five outer samples 

from each aspect for a total of 40 branch samples per tree. Calculate the proportion of terminals infested with at least one P. luteola egg and compare to Fig. 2. If the proportion of 

samples infested exceeds the selected line representing the desired level of fixed precision, 

sampling can be stopped. Otherwise, continue sampling branch terminals and adjusting the 

proportion of terminals infested until the appropriate line is crossed. This proportion (X) can 

then be used in the equation, Y = 0.97 + 17.42X, to predict the cumulative apparent damage 

(Y) of English elm later in the season.

 
46

Fig. 2. Sample sizes needed at two levels of precision (C) to estimate the proportion of 30-

cm branch terminals infested with eggs.

Figure 2 modified and reproduced with permission from Environmental Entomology, 

granted April 2, 2009.

Notes

The authors do not specify a minimum number of trees to sample, but sufficient trees should 

be sampled to get an average estimate of infestation levels before referencing Fig. 2. While 

no qualitative aesthetic injury has been set for P. luteola, >25% defoliation on English elm 
47

was chosen as a goal in northern California. This sampling method was not as effective on 

Siberian elm, Ulmus pumila L., possibly because Siberian elms tend to abscise partially defoliated leaves and produce new foliage.

The line representing C = 0.1 in the original Fig. 2 was removed to reduce scaling effects. 

The line for this higher level of precision can be calculated with the formula:

 

X =

2 yi

i = 1

n

∑

n

where n = number of samples needed, Po = proportion samples uninfested, Pi = proportion 

samples infested, and C = desired level of precision of estimate. There may not be sufficient 

trees to sample in an area of concern if P. luteola densities are low and a higher level of 

precision of the estimate is needed (Fig. 2). A manager might run out of trees to sample 

before sampling should stop.

A later study (Dahlsten et al. 1998) presents action thresholds and a refined sampling 

technique for P. luteola (see our summary in this volume).

Reference

* Dahlsten, D. L.; Rowney, D. L.; Lawson, A. B. 1998. IPM helps control elm leaf beetle. 

California Agriculture 52: 17-23.
48

Elm Leaf Beetle

Pyrrhalta (=Xanthogaleruca) luteola (Müller)

Coleoptera: Chrysomelidae

Dreistadt, S. H.; Dahlsten, D. L.; Rowney, D. L.; Tait, S. M.; Yokota, G. Y.; Copper, W. A. 

1991. Treatment of destructive elm leaf beetle should be timed by temperature. California 

Agriculture 45: 23-25.

Objective

To develop optimal timing of insecticide applications for P. luteola based on accumulated 

degree-days.

Abstract

Elm leaf beetle, Pyrrhalta (=Xanthogaleruca) luteola (Müller), is one of the most important 

pests of urban elms, Ulmus spp., in the western USA and Canada. Larvae injure the host tree 

by skeletonizing leaves. 

Management decisions for P. luteola in northern California can be timed using degree-day 

(DD) accumulation, although elms should still be sampled to confirm the presence of this 

pest. Eggs of P. luteola eclose between 370 and 600DD above the base temperature of 11°C, 

while densities of first and second instars peak around 700DD. Insecticide applications 

against larval P. luteola should be made around 700DD if larvae are present.

Sampling Procedure

Beginning March 1, monitor degree-day accumulation above the threshold temperature of 

11 ̊C. Densities of first and second instars peak around 700DD in northern California. Insecticide applications should be made around this time if P. luteola populations are present. 

Temperature data are available from the University of California’s Statewide IPM Project.

Notes

This research was conducted in northern California and the degree-day accumulation correlated to eclosion and peak density of young larvae may not be valid in other areas. Use these 

recommendations with caution until validated in other regions.
49

Elm Leaf Beetle

Pyrrhalta (=Xanthogaleruca) luteola (Müller)

Coleoptera: Chrysomelidae

Lawson, A. B.; Dahlsten, D. L. 2003. Implementation of a citywide monitoring program to 

base treatment decisions on elm leaf beetle abundance. Journal of Arboriculture 29: 34-41.

Objective

To evaluate the operational feasibility of a monitoring program for P. luteola on urban elms.

Abstract

Elm leaf beetle, Pyrrhalta (=Xanthogaleruca) luteola (Müller), is one of the most important 

pests of urban elms, Ulmus spp., in the U.S.A. and Canada. English (U. procera Salisbury) 

and Siberian elms (U. pumila L.) are more susceptible to damage by P. luteola than other 

elms. Larvae injure the host tree by skeletonizing leaves. Dahlsten et al. (1994) developed 

a monitoring program that successfully predicted damage to elm from estimates of P. luteola

egg mass density, but it had not been tested operationally. The program was evaluated in 

Sacramento, CA, from 1995 to 1999.

During the 5-year program, an average of only 11.3% of susceptible elms needed treatment, 

compared to the previous practice of treating all susceptible elms on a calendar basis. The 

monitoring program worked very well for the first and second generations of P. luteola, but 

not for the third generation. This was due mainly to low sample sizes and an inability to 

make accurate predictions of damage due to an extended oviposition period compared to 

the first two generations. Further research is needed to determine the threshold for the third 

generation. 

The monitoring data fit the model of Dahlsten et al. (1994) well and thus were included in 

the model. The optimal damage threshold was subsequently raised to >27% of tips infested 

for the second generation. No increase in the damage threshold of the first generation was 

necessary. Managers may use a threshold of 30% elm branch tips infested with egg masses 

for both the first and second generations to simplify monitoring for this pest. Treatment 

decisions can be based on the mean infestation in a stand rather than from individual trees. 

Overall, the monitoring program in Sacramento reduced pesticide usage by 90% citywide 

with savings of approximately $62,000 per year. This monitoring program, with its adjusted 

damage thresholds, is useful for area-wide management of P. luteola in California.
50

Sampling Procedure

Select English elm, Siberian elm, and their hybrids for sampling. Sampling in Sacramento 

was reduced to 20% of the susceptible elm species at the end of the evaluation program. See 

our reviews of Dreistadt et al. (1991) and Dahlsten et al. (1994) in this volume for information on sampling P. luteola, including when to sample and the number of branch tips and 

percentage of trees to sample in an area. Briefly, monitor degree-day accumulation above 

the threshold temperature of 11°C beginning March 1 (Dreistadt et al. 1991). Densities of 

first and second instars peak around 700DD in northern California (Dreistadt et al. 1991), so 

begin looking for egg masses before then.

Take 16 branch terminals, each 30-cm long, from each tree using pole pruners. Take 2 

branches from the inner canopy and 2 branches from the outer canopy in each cardinal 

direction. Note the presence of any egg masses on the samples. Consider treatment options 

when 30% of the sampled branch tips are infested with egg masses during the first or second 

generation of P. luteola. If a tree is found with an infestation near the treatment threshold, 

sample adjacent elms to delineate the area of infestation. Treatment decisions can be based 

on the mean infestation within a stand or on the mean infestation on individual trees.

Notes

English elm, Siberian elm, and their hybrids were included in this study. Caution should be 

used when implementing this monitoring program for other species of elm. A threshold of 

20–30% infested branch tips can be used with caution for the third generation of P. luteola, 

but additional work is need to verify the appropriateness of this threshold.

References

# Dahlsten, D.L.; Rowney, D.L.; Tait, S.M. 1994. Development of integrated pest management programs in urban forests: the elm leaf beetle (Xanthogaleruca luteola (Müller)) in 

California, USA. Forest Ecology and Management 65: 31-44.

# Dreistadt, S. H.; Dahlsten, D. L.; Rowney, D. L.; Tait, S. M.; Yokota, G. Y.; Copper, W. A. 

1991. Treatment of destructive elm leaf beetle should be timed by temperature. California 

Agriculture 45: 23-25.
51

Balsam Fir Sawfly

Neodiprion abietis (Harris)

Hymenoptera: Diprionidae

Struble, G. R. 1959. Egg sampling reveals trend in white-fir sawfly abundance. Journal of 

Forestry 57: 510-511.

Objective

To develop a sampling procedure for the eggs of N. abietis as a means of predicting larval 

damage on true fir trees.

Abstract

Balsam fir sawfly (=white-fir sawfly), Neodiprion abietis (Harris), is a defoliator of white fir, 

Abies concolor (Gord. & Glend.) Lindl., and California red fir, A. magnifica A. Murr. Female N. abietis deposit eggs in slits cut into needles of true firs. Overwintering egg clutches 

hatch in June; larvae feed in June and July as gregarious colonies on last year’s needles. The 

potential damage produced by larval N. abietis can be estimated by sampling egg clutches, 

which allows sufficient time to take control measures before larvae produce excessive defoliation.

Sampling Procedure

Divide selected sampling areas into four quadrants. In each quadrant, randomly select 25 

trees and clip two twigs from each tree for a total of 200 twig samples from the sampling 

area. Clip branches that can be reached from the ground. Each clipping should be about 

15.24 cm long with approximately 15 laterals. Keep clippings refrigerated until the number 

of clutches present can be tallied. A clutch of N. abietis eggs consists of all the eggs laid in 

the needles of one or more adjacent shoots on a twig, presumably laid by a single female. 

Sample trees between mid-May and early June, before larvae begin to hatch.

There is a direct correlation between the number of clutches observed and the level of defoliation produced by N. abietis larvae. Approximately 1,000 larvae are produced from a population of 20 clutches. A population of this size is likely to produce noticeable defoliation. 

More than 20 clutches per 200-twig sample from 100 trees indicates that control measures 

may be needed to limit defoliation in the sampling area.

Note

The author attributed a downward trend in the number of clutches observed over several years 

to a polyhedrosis virus attacking mature larvae.
52

Lyytikäinen-Saarenmaa, P.; Anderbrant, O.; Löfqvist, J.; Hedenström, E.; Högberg, H.-E. 

1999. Monitoring European pine sawfly population densities with pheromone traps in 

young pine plantations. Forest Ecology and Management 124: 113-121.

Objective

To monitor the density of N. sertifer in pine plantations using pheromone traps.

Abstract

European pine sawfly, Neodiprion sertifer (Geoffroy), was introduced into North America in 

1925 and now occurs throughout the north-central and northeastern USA and Canada. This 

pest attacks two- and three-needle pines (Pinus spp.). Larval N. sertifer feed in gregarious colonies and prefer to consume older foliage. Severe defoliation results in reduced tree 

growth and damaged aesthetic value, but trees usually survive because the current-year foliage is rarely attacked. Pheromone trapping could be used to track population densities over 

years and provide a means of predicting outbreaks.

A study was conducted in Sweden to evaluate the use of pheromone traps in monitoring 

populations of N. sertifer. The pheromone diprionyl acetate, (2S,3S,7S)-3,7-dimethyl-2-

pentadecyl acetate, was used in sticky traps at volumes of 1, 10 and 100 μg per trap. Traps 

loaded with 100 μg of diprionyl acetate caught the most males and this volume appeared 

optimal for monitoring N. sertifer. The best correlations between trap catches of male N. sertifer and larval density within the same or later generations were observed when the population was increasing, but not when declining to endemic levels. Because of the lack of strong 

correlations between male capture and larval density, annual pheromone trapping should be 

conducted in conjunction with other population surveys to monitor population trends. However, pheromone trapping still represents a method of predicting incipient outbreak densities 

with time to make management decisions for N. sertifer. 

Sampling Procedure

Randomly select three host trees in the center of each trapping site. Selected trees should 

form a triangle, with 30–50 m between trees. Install 1 Lund-I sticky trap 2 m above ground 

in each selected tree (Anderbrandt et al. 1989). Bait each trap with 100 μg of diprionyl 

acetate loaded on a dental cotton roll. The pheromone should have a release rate of 2.5 μg 

per day over a 30 day period. Leave traps in place during the male flight period, generally 

European Pine Sawfly

Neodiprion sertifer (Geoffroy)

Hymenoptera: Diprionidae
53

from early August to early October in Sweden. Reload the bait and replace the sticky bottom of each trap in September to ensure adequate coverage of the flight period. Trapping 

over successive years should provide trends in population levels allowing for the detection of 

outbreaks. Total trap catches that increase over multiple, consecutive years likely indicate an 

incipient outbreak of N. sertifer, but should be corroborated with data from surveys of eggs, 

larvae, or pupae.

Notes

This study was conducted in Sweden and the results may not be applicable in North America. 

Please use this trapping procedure with caution. A lure with a constant release rate would 

be preferable to dental cotton rolls, which dispense the pheromone at a decreasing rate with 

time.

References

Anderbrant, O.; Löfqvist, J.; Jönsson, J.; Marling, E. 1989. Effect of pheromone trap type, 

position, and colour on the catch of the pine sawfly Neodiprion sertifer (Geoffr.) (Hym. 

Diprionidae). Journal of Applied Entomology 107: 365-369.
54

Yellowheaded Spruce Sawfly

Pikonema alaskensis (Rohwer)

Hymenoptera: Tenthredinidae

Morse, B. W.; Kulman, H. M. 1985. Monitoring damage by yellowheaded spruce sawflies 

with sawfly and parasitoid pheromones. Environmental Entomology 14:131-133.

Objective

To classify the population phase of P. alaskensis as either increasing or decreasing through 

pheromone trapping of the pest and its major parasitoid.

Abstract

Yellowheaded sawfly, Pikonema alaskensis (Rohwer), is an important native defoliator of 

spruce (Picea spp.) in Alaska, Canada, and the northeastern U.S., including the Lake States. 

Open grown spruces, such as those in ornamental plantings, plantations, and shelterbelts, are 

particularly susceptible to P. alaskensis. A single year of defoliation by P. alaskensis may 

result in growth loss, while defoliation over multiple years causes branch dieback, topkill, or 

tree mortality. Outbreaks may persist for 3 years, followed by population decline in the postoutbreak phase.

Pheromone traps can be used to monitor population densities of P. alaskensis and its parasitoid Syndipnus rubiginosus Walley (Hymenoptera: Ichneumonidae). Research conducted on 

white spruce [Picea glauca (Moench) Voss] in northern Minnesota showed that the population phase of P. alaskensis can be determined using the formula log(I) = 1.36 – 0.17 log(S) 

– 0.25 Log(P) + 0.13 log (S*P) – 0.3 log (Ht), where I = a trend index based on defoliation, 

S = the number of trapped P. alaskensis, P = the number of trapped S. rubiginosus, S*P = the 

interaction between S and P, and Ht = tree height. The P. alaskensis population is classified 

as increasing when I >1 and decreasing when I <1. Land managers can use this technique to 

monitor and plan control measures for populations of P. alaskensis approaching an outbreak 

phase of population growth.

Sampling Procedure

Use (Z)-10-nonadecenal and ethyl palmitoleate [ethyl (Z)-9-hexadecenoate] as lures for P. 

alaskensis and S. rubiginosus, respectively. Lures can be sandwiched separately between 16-

mil polymeric films so that 1 cm2 of film releases 0.67 mg/cm2

 sawfly pheromone or 0.62 mg/

cm2 parasitoid pheromone. Bait Pherocon II pheromone sticky traps (Trécé, Adair, OK) with 

1 cm2

 of film of either the sawfly or parasitoid pheromone. Hang traps 1.5 m above ground 

in white spruce stands. Traps should be installed in early May for P. alaskensis and in early 
55

June for S. rubiginosus. Remove both traps after one month. Sort trap contents and record 

the cumulative numbers of trapped male P. alaskensis and S. rubiginosus. After trapping has 

ended, determine the average tree height of the trees surrounding each trap. 

Calculate the trend index for each group of trees surrounding each trap using the following 

formula:

log(I) = 1.36 – 0.17 log(S) – 0.25 Log(P) + 0.13 log (S*P) – 0.3 log (Ht)

where I = a trend index based on observed defoliation, S = the number of trapped P. alaskensis, P = the number of trapped S. rubiginosus, S*P = the interaction between S and P, and 

Ht = tree height. Classify populations of P. alaskensis as increasing (pre-outbreak) if I >1 or 

decreasing (post-outbreak) if I <1. Consider implementing control measures if the population is increasing. 

Notes

The authors presumably installed paired P. alaskensis and S. rubiginosus traps on the same 

tree within each plot but they did not specify how far apart trap trees should be separated 

nor did they recommend a specific number of traps to install in a stand. Clearly a sufficient 

number of traps must be installed throughout a stand to reduce variation among trap pairs and 

accurately classify P. alaskensis populations as increasing or decreasing. The model is applicable only to areas where S. rubiginosus occurs as a major parasitoid of P. alaskensis.
56

Larch Sawfly

Pristiphora erichsonii (Hartig)

Hymenoptera: Tenthredinidae

Ives, W. G. H.; Prentice, R. M. 1958. A sequential sampling technique for surveys of the 

larch sawfly. Canadian Entomologist 90: 331-338.

Objective

To describe a sequential sampling procedure for the eggs of P. erichsonii useful for predicting 

the infestation level and corresponding expected percentage defoliation.

Abstract

Larch sawfly, Pristiphora erichsonii (Hartig), is a significant defoliator of larch, Larix spp., 

across its range in North America, including plantations and ornamental plantings. Larvae 

feed in gregarious colonies and consume entire needles. Trees may releaf following defoliation, but severe defoliation results in thin crowns, branch dieback, and growth loss. Repeated 

defoliation over consecutive years may cause tree mortality. 

Female P. erichsonii lay eggs in small slits in the sides of new shoots, which often curl in a 

characteristic manner due to the injury. Even if shoots do not curl, the oviposition slits in 

new shoots are readily observable and are a means of estimating the density of P. erichsonii. 

A sequential sampling plan was developed for P. erichsonii eggs in larch stands in central 

Canada. The plan requires a minimum of 10 shoots and a maximum of 400 shoots to classify infestations as light, moderate, or severe. Light, moderate, and severe infestations correspond to <8, 12–22, and >28% of shoots used for oviposition and <20, 30–60, and >70% 

expected defoliation, respectively.

Sampling Procedure

Sample trees after oviposition has ended, usually in mid-August. Randomly select larch trees 

in each plot. Using pole pruners, sample two whole branches from the mid-crown of each 

tree. Starting at the apical tip, examine the current-year shoots on each branch in groups 

of 10 shoots. If the last group of shoots on the first branch does not equal 10, carry over to 

the second branch. Record the number of shoots in each group of 10 that show oviposition 

damage (i.e., curling and/or egg slits) as a cumulative tally. Reference Table 3 or Fig. 1 after 

processing each group of 10 shoots. Stop sampling when the cumulative tally falls out of 

either the light-moderate or moderate-severe bands in Table 3, or crosses one of the decision 

lines into the light, moderate, or severe areas of Fig. 1. If 400 shoots are examined without 

reaching a decision, and the cumulative count in Table 3 remains in the light-moderate band, 
57

classify the infestation as light if the count is <39 but as moderate if >40. If the cumulative 

count remains in the moderate-severe band, classify the infestation as moderate if the count 

is <99 and severe if >100. Refer to Table 2 to determine the approximate expected percentage of defoliation corresponding to the infestation level and observed percentage of damaged 

shoots.

Note

Severe defoliation by P. erichsonii may result in decreased shoot production with changes in 

the relationship of pest density to expected defoliation.

Fig. 1. Sequential graph for sampling larch sawfly populations.
58

Table 2. Larch sawfly infestation classes based on the proportion of shoots utilized for oviposition and the percentage of defoliation.

Infestation class Proportion of shoots utilized 

for oviposition

Approximate percentage of 

defoliation

Light <0.08 <20

Moderate 0.12–0.22 30–60

Severe >0.28 >70

Table 3. Sequential table for use by field parties in classifying infestations. Sampling continues until the cumulative numer of shoots utilized for oviposition falls outside of either of the 

tow bands into one of the three zones.

Number of 

shoots examined

Cumulative number of shoots utilized for oviposition

 Light-moderate band Moderate-severe band

10

LIGHT ZONE

–

MODERATE ZONE

–9

SEVERE ZONE

20 – –11

30 – –14

40 – –16

50 1– –19

60 2– –21

70 3– –24

80 4–12 14–26

90 5–13 16–29

100 5–14 19–31

110 6–15 21–34

120 7–16 24–36

130 8–17 26–39

140 9–18 29–41

150 10–19 31–44

160 11–20 34–46

170 12–21 36–49

180 13–22 39–51

190 14–23 41–54

200 15–24 43-56

210 16–25 46–59

220 17–26 48–61

230 18–27 51–64

240 19–28 53–66

250 20–29 56–69
59

Number of 

shoots examined

Cumulative number of shoots utilized for oviposition

 Light-moderate band Moderate-severe band

260

LIGHT ZONE

21–30

MODRATE ZONE

58–71

SEVERE ZONE

270 22–31 61–74

280 23–32 63–76

290 24–33 66–79

300 25–34 69–81

310 26–35 71–84

320 27–36 73–86

330 28–37 76–89

340 29–38 78–91

350 30–39 81–94

360 31–40 83–96

370 32–41 86–99

380 33–42 89–101

390 34–43 91–104

400 35–44 93–106

Figure 1 and Tables 2 and 3 are reprinted as fair use under 17 USC § 107.
60

Fall Cankerworm

Alsophila pometaria (Harris)

Lepidoptera: Geometridae

Hébert, C.; St. Antoine, L. 1999. Oviposition trap to sample eggs of Operophtera bruceata

(Lepidoptera: Geometridae) and other wingless geometrid species. Canadian Entomologist 

131: 557-565.

Objective

To develop an oviposition trap for A. pometaria that could be used in large scale population 

monitoring.

Abstract

Fall cankerworm, Alsophila pometaria (Harris), is a significant defoliator of northern red oak, 

Quercus rubra L., and white oaks (Quercus spp.), maples (Acer spp.), elms (Ulmus spp.), 

hickories (Carya spp.), ashes (Fraxinus spp.) and Prunus spp. in Canada and the northern 

U.S. Fall cankerworm can cause a reduction in growth, mast production, and even tree mortality. In public areas, defoliation reduces the aesthetic value of infested trees.

A new egg sampling method for wingless geometrid moths that uses an oviposition trap has 

been refined from earlier work (Hébert & St. Antoine 1998), where females lay eggs on a 

polyurethane foam band covered by a Multi-Pher® plate. This trap was initially devised for 

monitoring populations of the Bruce spanworm, Operophtera bruceata (Hulst), but was also 

useful for surveying populations of A. pometaria. Density of A. pometaria eggs on oviposition traps was higher than adult females on sticky traps, and the oviposition traps were more 

effective in detecting low populations of A. pometaria. Moreover, oviposition traps did not 

become saturated at high population densities of A. pometaria, a phenomenon that occurred 

regularly with sticky bands used to trap adult females. Sub-sampling eggs on the polyurethane foam band is recommended when egg densities on the traps are high. This trap is also 

useful for monitoring populations of winter moth, Operophtera brumata (Linnaeus). 

Sampling Procedure

The standard trap is a 1.2 m post of black ABS (acrylonitrile butadiene styrene) pipe, with a 

10 x 31 cm band of polyurethane foam attached lengthwise around the top of the post. Each 

trap has a model I Multi Pher trap with a closed funnel placed on top of the post. Posts are 

sandblasted to ensure that the wingless females can climb to the oviposition strip. Trap 

placement and density will depend on the objective(s) of the sampling effort. Traps should 

be installed before the oviposition period of A. pometaria and collected after oviposition has 
61

ceased. Examine collected foam strips and count the number of eggs laid on them with the 

aid of a light table. Sub-sampling the foam strips is often necessary to reduce effort when 

moth populations are high. Divide the foam strip into 30 pieces measuring 1 x 10 cm each. 

Randomly select and count the eggs on three pieces, which is the recommended sub-sample 

size.

Notes

Several wingless geometrids could lay eggs on the foam strips, causing a bias in estimation 

of the population of the targeted insect pest. An insecticidal strip in the Multi-Pher trap to 

trap adult females might help determine the relative contribution of eggs by species. The 

eggs of A. pometaria are black while those of Operophtera bruceata are orange.

References

* Hébert, C.; St. Antoine, L. 1998. The oviposition trap: a new technique for sampling eggs 

of the Bruce spanworm and similar species. Res. Notes 5. Canadian Forest Service, Laurentian Forestry Centre; 4 p. 
62

Eastern Hemlock Looper

Lambdina fiscellaria (Guenée)

Lepidoptera: Geometridae

Hébert, C.; St. Jobin, L.; Auger, M.; Dupont, A. 2003. Oviposition traps to survey eggs of 

Lambdina fiscellaria (Lepidoptera: Geometridae). Journal of Economic Entomology 96: 

768-776.

Objective

To develop an oviposition trap for L. fiscellaria that could be used to monitor populations and 

detect outbreaks.

Abstract

Hemlock looper, Lambdina fiscellaria (Guenée), is an important native defoliator of balsam 

fir [Abies balsamea (L.) Mill.], hemlock (Tsuga spp.), and white spruce [Picea glauca (Moench) Voss] in eastern USA and Canada. Young larvae feed on a variety of hosts but have 

greater survival on young balsam fir needles. Older larvae feed indiscriminately. Periodical damage in balsam fir stands generally produces defoliation, growth reduction, and tree 

mortality. Outbreaks of L. fiscellaria build and subside quickly, but tree mortality can occur 

during the first year damage is found.

Egg density in a known volume of bark, epiphytic lichens, and moss or on 1-m branch 

samples has been used to monitor populations of L. fiscellaria, but these methods require an 

extraction process to remove and collect the eggs from the substrate (Otvos & Bryant 1972; 

Dobesberger 1989; Shore 1990). The use of an artificial substrate as an oviposition trap was 

explored for L. fiscellaria in Québec, Canada. White polyurethane foam, alone or in conjunction with a Luminoc® light trap (Comlab Inc., Québec City, Québec, Canada), was an 

attractive substrate for female L. fiscellaria and facilitated the determination of egg density 

without a time-consuming extraction process. Furthermore, these traps are not influenced by 

the presence of epiphytic lichens that occur in varying amounts on the 1-m branch samples 

and influence L. fiscellaria oviposition. The modified light-trap can detect increases in L. fiscellaria populations while populations are still at very low densities. The passive foam strips 

offer the advantage of being inexpensive and very easy to install.

Sampling Procedure

Modified light trap: Wrap white polyurethane foam around a drainage cylinder (10 cm diam. 

x 27.5 cm long). Cut two vertical rows of three 5-cm diameter holes through the foam and 

the cylinder wall. Place a 10 x 30 cm strip of white foam inside the cylinder. Modify the 
63

housing of a Luminoc® light trap by cutting away part of the bottom section. Attach the 

drainage cylinder to the bottom of the modified light trap. Moths attracted to the light trap 

should be able to freely enter the drainage cylinder through the two vertical rows of holes. 

Hang light traps with attached cylinders on balsam fir branches 3–4 m above ground. Install 

traps in early August before the female flight period begins and operate through the oviposition period, removing the traps in mid-October. Light traps should run throughout the night. 

Replace the 6-V batteries after 2 weeks to ensure that traps remain operational. 

Passive oviposition trap: Staple the top of a 20 x 45 cm strip of white polyurethane foam directly to a tree trunk at breast height. The 20 cm bottom edge should be free to allow females 

to oviposit on the underside of the foam strip. Install traps before the female flight period 

begins and leave in place through the oviposition period.

At the end of the trapping period, remove the white foam from either the modified light trap 

or from tree trunks and examine on a light table. Classify eggs as follows and tally the number of eggs in each state: 

Egg state Description

Unhatched Brown, intact

Hatched Translucent chorion with hole at operculum

Parasitized Black and intact, or mostly empty chorion with a dark spot and a hole at operculum

Land managers can use these methods to monitor L. fiscellaria over multiple years and detect 

population changes before an outbreak. Eggs collected on the foam can be reared for study 

in the laboratory, unlike eggs that are extracted using hot water or bleach solutions.

Notes

The authors did not recommend a specific number of foam strips to use on each tree or the 

number of traps to install in each plot. Traps in this study were set on transect lines through 

plots with ≤40 m between traps.

References

* Dobesberger, E. J. 1989. A sequential decision plan for the management of the eastern hemlock looper, Lambdina fiscellaria fiscellaria (Lepidoptera: Geometridae), in Newfoundland. 

Canadian Journal of Forest Research 19: 911-916.

* Otvos, I. S.; Bryant, D. G. 1972. An extraction method for rapid sampling of eastern hemlock looper eggs, Lambdina fiscellaria fiscellaria (Lepidoptera: Geometridae). Canadian 

Entomologist 104: 1511-1514.

# Shore, T. L. 1990. Recommendations for sampling and extracting eggs of the western 

hemlock looper, Lambdina fiscellaria lugubrosa (Lepidoptera: Geometridae). Journal of the 

Entomological Society of British Columbia 87: 30-35.
64

Western Hemlock Looper

Lambdina fiscellaria lugubrosa (Hulst)

Lepidoptera: Geometridae

Evenden, M. L.; Borden, J. H.; Van Sickle, G. A.; Gries, G. 1995a. Development of a pheromone-based monitoring system for western hemlock looper (Lepidoptera: Geometridae): 

effect of pheromone dose, lure age, and trap type. Environmental Entomology 24: 923-932.

Objective

To evaluate appropriate type of trap, dose of pheromone, and lure longevity for L. fiscellaria 

lugubrosa.

Abstract

Western hemlock looper, Lambdina fiscellaria lugubrosa (Hulst), is an important periodic defoliator of western hemlock, Tsuga heterophylla (Raf.) Sarg., and other conifers in the United 

States and Canada. Damage generally occurs in mature or senescing stands where defoliation results in growth reduction, top kill, and tree mortality. 

A pheromone-based monitoring system could be established for L. fiscellaria lugubrosa, 

which is strongly attracted to the sex pheromone of eastern hemlock looper, Lambdina fiscellaria fiscellaria (Guenée). This pheromone, a 1:1 ratio of isomeric 5,11-dimethylheptadecane 

and 2,5-dimethylheptadecane, was tested at doses of 10, 100, 1,000, and 10,000 μg per trap. 

Trap saturation appeared to occur with the 1,000 and 10,000 μg doses, while traps baited with 

1 or 10 μg were effective over the flight period of L. fiscellaria lugubrosa. Traps baited with 

10 μg pheromone provide a better estimate of the size of the next generation and are therefore appropriate for a pheromone-based monitoring system (Evenden 1994, Evenden et al.

1995). Nonsticky Unitraps (Pherotech, Delta, BC) were recommended over sticky traps for 

monitoring L. fiscellaria lugubrosa. The inner surface of sticky traps, made from 2-liter milk 

cartons shaped into 3-sided traps and coated with adhesive on the interior, became saturated 

with as few as 100 moths. A pheromone-based monitoring system using 10 μg pheromone in 

Unitraps would allow managers to detect increasing populations of L. fiscellaria lugubrosa

during the adult flight period in late summer instead of during egg surveys in October. The 

earlier detection would allow for additional time to make management decisions for this pest. 

Sampling Procedure

Install Unitraps baited with rubber septa loaded with 10 μg of 1:1 ratio of isomeric 5,11-dimethylheptadecane and 2,5-dimethylheptadecane. Mount septa in the lid. Traps should be 

installed in trees 1–2 m above ground and at least 100 m apart from each other. Deploy traps 
65

before the L. fiscellaria lugubrosa flight period, which begins in late July or early August, 

and leave in place through October. Check traps every 2–3 weeks. Solid-formulated cubes 

of dichlorovos placed in each trap will kill captured moths; replace dichlorovos each time 

traps are checked.

References

Evenden, M. L. 1994. Devleopment of a pheromone-based monitoring and detection technique for the western hemlock looper, Lambdina fiscellaria lugubrosa (Hulst) (Lepidoptera: Geometridae). M.P.M. thesis, Simon Fraser University, Burnaby, BC, Canada.

Evenden, M. L.; Borden, J. H.; Van Sickle, G. A. 1995b. Predictive capabilities of a pheromone-based monitoring system for the western hemlock looper, (Lepidoptera: Geometridae). Environmental Entomology 24: 933-943.
66

Western Hemlock Looper

Lambdina fiscellaria lugubrosa (Hulst)

Lepidoptera: Geometridae

Liang, Q.; Otvos, I. S.; Bradfield, G. E. 1997. Forest roadside sampling of larvae and adults 

of the western hemlock looper, Lambdina fiscellaria lugubrosa. Forest Ecology and Management 93: 45-53.

Objectives

To compare densities of L. fiscellaria lugubrosa along roads and within stands; to determine 

the optimum distance between adjacent sampling sites along roads.

Abstract

Western hemlock looper, Lambdina fiscellaria lugubrosa (Hulst), is an important defoliator 

of western hemlock, Tsuga heterophylla (Raf.) Sarg., and other conifers in the United States 

and Canada. Periodical damage generally occurs in mature or senescing stands, where defoliation results in growth reduction, top kill, and tree mortality. 

Research conducted in British Columbia suggests that L. fiscellaria lugubrosa populations 

can be surveyed along roads, which has the advantage of assessing large areas in less time 

compared to surveys conducted within stands. Pheromone trap captures and larval sampling 

were compared along roads and within stands in the Kamloops Forest Region. Larval densities along roads were positively related to those within stands (r2

 = 0.97; P < 0.05). Likewise, 

pheromone trap catches along roads were closely related to trap catches within stands 

(r2

 = 0.88; P < 0.05). These results suggest a means of predicting population trends using 

roadside sites, but additional research is required before population densities within stands 

can be predicted using roadside densities. 

Spatial analysis of pheromone trap catches along roads in three Vancouver watersheds indicated that male moth captures remained similar when traps were placed less than 3 km apart 

along roads. In this region, pheromone traps should be placed at least 3 km apart along roads 

to reduce the number of necessary sampling sites and maximize sampling effort. However, 

spatial analysis of the data from the Kamloops site indicated that trap catches were similar 

when installed up to 7.25 km apart. At this location pheromone traps should be installed at 

least 7.25 km apart on roadways to maximize sampling effort. Clearly, the optimum distance 

between adjacent sampling points may vary among locations and should be validated for new 

areas as well as modified with new data when available.
67

Sampling Procedure

Larval sampling: Select sampling sites along roads at least 3 km apart. At each site, randomly select two western hemlock trees accessible from the road. Cut two 45-cm branch 

tips from the lower crown of each tree using pole pruners with an attached collection basket. 

Examine branch tips for larvae.

Adult trapping: Select sampling sites along roads at least 3 km apart. Place one Multi-trap 

baited with 200 μg of a 1:1 ratio of isomeric 5,11-dimethylheptadecane and 2,5- dimethylheptadecane (Pherotech,Vancouver, B.C.) at each sampling site. Hang traps in the tree canopy approximately 1.5 m above ground. Traps should be installed before adult emergence.

Note

The optimum distance between adjacent sampling sites may vary among regions. The recommended distance of 3 km between sites may not be appropriate outside of Vancouver, B.C
68

Western Hemlock Looper

Lambdina fiscellaria lugubrosa (Hulst)

Lepidoptera: Geometridae

Liang, Q.; Otvos, I. S.; Bradfield, G. E. 1998. Pupal sampling of the western hemlock looper, 

Lambdina fiscellaria lugubrosa (Hulst) (Lep., Geometridae) using burlap traps. Journal of 

Applied Entomology 122: 85-88.

Objectives

To test the efficacy of three types of burlap traps in capturing pupating L. fiscellaria lugubrosa larvae; to validate trap capture as a predictor of population density.

Abstract

Western hemlock looper, Lambdina fiscellaria lugubrosa (Hulst), is an important defoliator of 

western hemlock, Tsuga heterophylla (Raf.) Sarg., and other conifers in the United States and 

Canada. Periodical damage generally occurs in mature or senescing stands, where defoliation results in growth reduction, top kill, and tree mortality.

Burlap bands are used to collect L. fiscellaria lugubrosa pupae, but trap catches have not 

been validated as an estimator of population densities. In 1993 and 1994, three types of 

burlap traps with different designs were tested in British Columbia to evaluate trap efficiency 

in capturing pupating larvae. Traps varied in the number of folds and type of pockets in the 

burlap material.

Trap capture in a commercially available burlap trap folded once was related positively to L. 

fiscellaria lugubrosa population density. A pooled regression model based on data from three 

locations was developed to relate the number of pupae caught in a trap to the pupal counts 

recorded from a 30 cm x 40 cm area of bark on nearby trees:

Loge

(y + 1) = 0.6199loge(x + 1)

where x = the number of pupae per 100 cm2 of burlap trap surface area and y = the pupal 

counts from a 30 cm x 40 cm area of bark of a nearby tree. This model showed a reasonable 

fit of r2 = 0.79 (MSE = 0.0079, n = 90) for the pooled data, indicating that trap capture was a 

good estimator of actual population density.

Sampling Procedure

Traps are commercially available burlap bands (1-m wide) folded over once with the fold 
69

located at the bottom of the trap and the opening oriented towards the canopy. Install burlap 

traps by wrapping once around the trunk at 1.3 m above ground. Study details, including plot 

characteristics and the pupal sampling procedure, are described in Liang (1997). Express 

pupal density as the number of pupae per 100 cm2

 of trap surface area. Use the following 

formula to estimate the actual population of L. fiscellaria lugubrosa: 

Loge(y + 1) = 0.6199loge(x + 1)

where x = the number of pupae captured in a burlap trap and y = the pupal counts from a 30 

cm x 40 cm area of bark.

Reference

Liang, Q. 1997. Sampling methods and population prediction in Lambdina fiscellaria lugubrosa, in British Colombia. Ph.D. thesis, University of British Colombia, 176 pp.
70

Western Hemlock Looper

Lambdina fiscellaria lugubrosa (Hulst)

Lepidoptera: Geometridae

Shepherd, R. F.; Gray, T. G. 1972. Solution separation and maximum likelihood density estimates of hemlock looper (Lepidoptera: Geometridae) eggs in moss. Canadian Entomologist 104: 751-754.

Objective

To describe an efficient method of processing L. fiscellaria lugubrosa eggs in moss samples 

and determining egg density.

Abstract

Western hemlock looper, Lambdina fiscellaria lugubrosa (Hulst), is an important defoliator of western hemlock, Tsuga heterophylla (Raf.) Sarg., and other conifers in the USA and 

Canada. Periodical damage generally occurs in mature or senescing stands, where defoliation results in growth reduction, top kill, and tree mortality. Eggs are laid on moss, bark, and 

organic debris on the trunks and branches of trees and on the ground. Visual surveys of eggs 

in moss samples are tedious and often inaccurate as not all eggs are easily seen and empty 

chorions can be mistaken for viable eggs. A two-step separation technique using solutions 

of NaOH and NaCl was developed to process L. fiscellaria lugubrosa more efficiently. Eggs 

obtained in this manner can be used to determine egg density per unit of sampled moss or 

duff. Collected eggs can be reared if the NaOH soak is skipped. The entire process of soaking, rinsing, and separating a sample took an average of 15–20 minutes, not including time 

spent counting eggs on the filter paper.

The maximum likelihood method of sampling (MLM; Southwood 1966) can be used to determine the density of eggs in each moss sample. The MLM assumes that a constant proportion of eggs will be removed from the sample with each wash, but removing all eggs is not 

necessary. This method requires that each wash in the separation technique take exactly 2 

minutes with the same amount of effort for each wash in each sample. This technique was 

evaluated with 35,464 cm2

 samples of moss and was considered to be efficient in estimating 

egg densities. Assuming a population of 200 eggs in each sample, 75% of the eggs must be 

collected by the three washes to obtain a coefficient of variation of ≤10%. This was exceeded in that the technique removed an average of >98% of the eggs in each sample.

Sampling Procedure

Collect 464.5 cm2 samples of moss from the bark surface of hemlock trees. Soak each sam-
71

ple for one minute in separate containers filled with 0.5% NaOH solution. For each container, pour the contents onto a set of circular mesh screens, 20 cm wide, with the top and bottom 

screens having 1.00- and 0.354-mm mesh openings, respectively. Rinse the container with 

water and pour onto the mesh as well. Mix the moss on the upper screen continuously for 

exactly two minutes while irrigating the material with water from a hose at full tap pressure. 

Separate the two screens and wash the eggs and other fine debris off the bottom screen into 

a collection jar. Join the two screens again and wash the moss sample on the upper screen 

for exactly two minutes twice more, washing the eggs and other debris into the collection jar 

each time. Pour the contents of the collection jar onto a 12.4-cm disk of filter paper set in a 

Buchner vacuum funnel. Vacuum filtrate the contents, then count the number of eggs on the 

filter paper using a stereomicroscope.

Samples of moss or duff under trees require differential floatation to separate L. fiscellaria 

lugubrosa eggs from the very fine organic debris. After vacuum filtration, use a 15% NaCl 

solution to wash the eggs and fine material off the filter paper and into a 500-ml separatory 

funnel. Add enough 15% NaCl solution that the contents can mix easily and shake the funnel 

well. Intact eggs should float to the surface while empty chorions and other extraneous material sink to the bottom. Loosen the stopcock to remove this material from the funnel. The 

upper solution with intact eggs can be poured onto a 12.4-cm disk of filter paper set in a Buchner vacuum funnel. Rinse with additional NaCl solution and pour the rinsate onto the filter 

paper. Vacuum filtrate the contents, then count the number of eggs on the filter paper using a 

stereomicroscope. The NaCl solution can be reused for subsequent separations.

Note

Exposures of longer than one minute to the NaOH soak will dissolve the chorions and degrade the eggs, leading to inaccurate counts of egg densities.

Reference

Southwood, T. R. E. 1966. Ecological methods with particular reference to the study of insect 

populations. London: Methuen; 391 p.
72

Western Hemlock Looper

Lambdina fiscellaria lugubrosa (Hulst)

Lepidoptera: Geometridae

Shore, T. L. 1990. Recommendations for sampling and extracting the eggs of the western 

hemlock looper, Lambdina fiscellaria lugubrosa (Lepidoptera: Geometridae). Journal of 

the Entomological Society of British Columbia 87: 30-35.

Objectives

To compare methods of extracting eggs of L. fiscellaria lugubrosa from lichen and to determine the vertical distribution of eggs among crown levels on western hemlock.

Abstract

Western hemlock looper, Lambdina fiscellaria lugubrosa (Hulst), is an important defoliator of western hemlock, Tsuga heterophylla (Raf.) Sarg., and other conifers in the United 

States and Canada. Periodical damage generally occurs in mature or senescing stands, where 

defoliation results in growth reduction, top kill, and tree mortality. In interior forests of British Columbia, females oviposit on lichens present on western hemlock branches (Thomson 

1958). 

The efficiency of extracting L. fiscellaria lugubrosa eggs from 100 g samples of lichen using 

a hot water wash was compared to a 2% chlorine bleach extraction. The hot water extraction 

removed more eggs from the lichen than the bleach extraction in removing eggs. However, 

the hot water extraction kills the eggs while the bleach extraction does not. The bleach extraction was preferable if eggs were to be reared to measure levels of fertility or identify any 

parasites present.

No differences in egg densities were found among the lower, mid-, or upper crowns of western hemlock. Lichen samples from the lower crown provide good estimates of egg density 

and are more accessible than samples from higher portions of the crown.

Sampling Procedure

Use pole pruners to sample lichen-covered branches randomly from the lower crowns of 

western hemlock. Remove the lichen from the branches and extract the eggs of L. fiscellaria 

lugubrosa using either the hot water or chlorine bleach extraction, depending on the research 

objectives.

Hot water extraction: Weigh the lichen sample. Place the lichen in a 2-liter plastic container 
73

and cover completely with 100° C water. Agitate contents to loosen the eggs from the lichen. 

Strain the contents of the bucket through a 1000 micron sieve to remove the organic debris. 

Strain the water a second time through a 250 micron sieve to collect the eggs. Repeat the hot 

water extraction using the organic debris remaining in the first sieve. Finally, rinse the contents of the 250 micron sieve into a glass jar. Use vacuum filtration to extract the eggs from 

the rinsate onto filter paper. Count the number of eggs present using a dissecting microscope. 

Express egg density as the total number of eggs collected in the initial weight of the lichen 

sample (usually a 100 g sample). Refer to Table 1 to classify eggs as healthy, parasitized, 

infertile, or old using egg color.

Table 1. Color characteristics of western hemlock looper egg types removed from lichen by 

the bleach or hot water methods

Type of egg Bleach method1 Hot water method

Healthy Brown Bronze

Parasitized Black Black

Infertile Green Yellow

Old Opaque Opaque 1

 From Ovtos and Bryant (1972).

Table 1 reproduced with permission from the author, granted April 7, 2009.

Chlorine bleach extraction: Weigh the lichen sample. Pull lichens apart and place in a 2-liter 

plastic container. Cover the lichen completely with a solution of 2% chlorine bleach. Agitate the container mechanically for 45 minutes at the lowest possible setting to loosen eggs 

from the lichen. Strain the contents of the bucket through a 1000 micron sieve to remove the 

organic debris. Strain the bleach solution a second time through a 250 micron sieve to collect 

the eggs. Repeat the bleach extraction using the organic debris remaining in the first sieve. 

Rinse the second sieve with tap water for 10 minutes to remove all traces of bleach. Finally, 

rinse the contents of the 250 micron sieve into a glass jar. Use vacuum filtration to extract 

the eggs from the rinsate onto filter paper. Count the number of eggs present using a dissecting microscope. Express egg density as the total number of eggs collected in the initial 

weight of the lichen sample (usually a 100 g sample). Eggs can be reared on moistened filter 

paper at 0°C for two months followed by 20°C until hatch to determine viability and parasitism, or refer to Table 1 to classify eggs as healthy, parasitized, infertile, or old using egg 

color. 

References

* Otvos, I. S.; Bryant, D. G. 1972. An extraction method for rapid sampling of eastern hemlock looper eggs, Lambdina fiscellaria fiscellaria (Lepidoptera: Geometridae). Canadian 

Entomologist 104: 1511-1514.

Thompson, M. G. 1958. Egg sampling for the western hemlock looper. Forestry Chronicle 

34: 248-256.
74

Bruce Spanworm Operophtera bruceata (Hulst)

Winter Moth Operophtera brumata (L.)

Lepidoptera: Geometridae

Fitzpatrick, S. M.; Troubridge, J. T.; Peterson, B. 1991. Distribution of European winter 

moth, Operophtera brumata (L.), and Bruce spanworm, O. bruceata (Hulst), in the lower 

Fraser Valley, British Columbia. Journal of the Entomological Society of British Columbia 

88: 39-45.

Objective

To present characters used to differentiate between male O. bruceata and O. brumata trapped 

in pheromone-baited traps. 

Abstract

Winter moth, Operophtera brumata (Linnaeus), is a significant defoliator of northern red oak, 

Quercus rubra L., maple (Acer spp.), and aspen (Populus spp.). It was introduced accidentally to Nova Scotia from Europe in 1930, and has expanded its range to include most of eastern 

Canada, portions of the northeastern US, and western Canada. It is a serious pest in commercial berry fields in British Columbia. Traps baited with the sex pheromone (Z,Z,Z)-1,3,6,9-

nonadecatetraene were placed in a mixed coniferous/deciduous forest and in commercial 

raspberry and blueberry fields in the lower Fraser Valley of British Columbia. This pheromone is attractive to males of O. brumata and the related Bruce spanworm, Operophtera bruceata (Hulst), which is a defoliator of various hardwood trees in North America. The baited 

traps collected both O. bruceata and O. brumata as well as some individuals with intermediate characters, suggesting that hybridization does occur between the two species. Adult O. 

bruceata and O. brumata are similar morphologically and can be difficult to separate to species. The authors present external and genitalic characters useful in differentiating between 

male O. bruceata and O. brumata. Both species are pests and monitoring each population is 

of importance. 

Sampling Procedure

Use the external and genitalic characters given in Table 1 to separate male O. bruceata and 

O. brumata caught in pheromone-baited traps. 

Reference

Eidt, D. C.; Embree, D. G.; Smith, C. C. 1966. Distinguishing adults of the winter moth 

Operophtera brumata (L.), and Bruce spanworm O. bruceata (Hulst) (Lepidoptera: 

Geometridae). Canadian Entomologist 98: 258-261.
75

Table 1. Characters used to separate males of the Bruce spanworm, Operophtera bruceata

(Hulst), from males of the European winter moth, Operophtera brumata (L.) 

Type of Character Spanworm Winter Moth

External

Wings

distinct lines and bands on 

dorsal surfaces

lines on dorsal forewing are 

obscure; often no lines on 

dorsal hindwing

Forewing pale yellow-orange ventral 

costal margin1

yellow-orange color faint to 

absent

Hindwing* dark dorsal discal dot dot absent

Abdomen* golden brown2 brown

Genitalic3

Uncus narrow (<0.12 mm); nearly 

parallel-sided; not spatulate

wider (ca. 0.14 mm): slightly 

spatulate

Juxta shallow medial notch at base; 

dorsal process wide (ca. 0.25 

mm) at base

divided at base by a medial 

cleft; dorsal process narrowed (ca. 0.16 mm) at base

Saccus long (ca. 0.63 mm); as long 

as or longer than width at 

base of valva

short (ca. 0.40 mm); shorter 

than width at base of valva

* Previously unreported chracters

1, 2 True only of Bruce spanworms in B.C.

3 Genitalic characters are illustrated in Eidt et al. (1966).

Table 1 reproduced with permission from the authors, granted April 6, 2009.
76

Bruce Spanworm, Operophtera bruceata (Hulst)

Winter Moth, Operophtera brumata (L.)

Lepidoptera: Geometridae

Pivnick, K. A.; Barton, D. L.; Millar, J. G.; Underhill, E. W. 1988. Improved pheromone trap 

exclusion of the Bruce spanworm Operophtera bruceata (Hulst) (Lepidoptera: Geometridae) when monitoring winter moth Operophtera brumata (L.) populations. Canadian 

Entomologist 120: 389-396.

Objectives

To improve the exclusion of O. bruceata from pheromone traps baited for O. brumata

through the addition of an inhibitor for O. bruceata.

Abstract

Winter moth, Operophtera brumata (Linnaeus), is a significant defoliator of northern red oak, 

Quercus rubra L., in Nova Scotia where it was introduced accidentally in 1930. The range of 

O. brumata has expanded to include most of eastern Canada, portions of the northeastern US, 

and western Canada. Adults of O. brumata and the related Bruce spanworm, Operophtera 

bruceata (Hulst), are similar morphologically. Operophtera bruceata (Hulst), is a major 

defoliator of maple (Acer spp.), aspen (Populus spp.), and Prunus spp. throughout Canada. 

The sex pheromone of O. brumata, (Z,Z,Z)-1,3,6,9-nonadecatetraene (1,3Z,6Z,9Z-19:H), also 

attracts male O. bruceata. The two species are nearly identical in appearance and require 

examination of the genitalia for proper identification. Both species are pests and monitoring 

each population is of importance. 

The addition of (E,Z,Z)-1,3,6,6-nonadecatetraene (1,3E,6Z,9Z-19:H) inhibits the attraction 

of O. bruceata to pheromone traps (Underhill et al. 1987). The discovery of the inhibitor 

1,3E,6Z,9Z-19:H facilitates the identification and separation of O. bruceata and O. brumata

populations, but traps baited with 1,3Z,6Z,9Z-19:H and 1,3E,6Z,9Z-19:H still capture a certain percentage of O. bruceata. Placement of the inhibitor inside the trap reduced capture of 

O. bruceata by 82% while placement of the inhibitor outside the trap reduced capture of O. 

bruceata by 97%. Contact with the inhibitor did reduce capture of O. brumata, signaling the 

need for careful placement of the inhibitor on the exterior of the trap. Research suggests that 

hybridization between O. brumata and O. bruceata does occur in areas where both species 

are present. The response of hybrid O. brumata x bruceata appears to be intermediate between the two species.
77

Sampling Procedure

Load 100 μg of a 1% solution of 1,3Z,6Z,9Z-19:H in hexane on rubber septa. Load 50 μg of 

a 1% solution of 1,3E,6Z,9Z-19:H in hexane on rubber septa. To each septum loaded with 

either the sex pheromone or the inhibitor, add 1–2 drops of both hexane and 10% butylated 

hydroxytoluene in acetone as an antioxidant. Place a septum loaded with 1,3Z,6Z,9Z-19:H 

inside each Hara trap, along with a trip of insecticide to kill attracted moths. Cover both ends 

of the trap, except for the entrance holes, with fiberglass mesh (1.5-mm mesh) to prevent 

predators from scavenging moths from the traps. Place 4 septa loaded with 1,3E,6Z,9Z-19:H 

on the exterior of each trap, with one septa placed above and one below the two entrance 

holes. Install traps about 1.5 m above ground and set at least 5 m apart in stands of host 

trees. Traps should be installed before the adult flight period in the fall.

Notes

Trapped moths can be identified to species using characteristics of the adult genitalia (Eidt 

et al. 1966; Fitzpatrick et al. 1991). Hybrid O. brumata x bruceata will be inhibited by 

1,3E,6Z,9Z-19:H to a lesser extent than O. bruceata, but more so than O. brumata.

References

Eidt, D. C.; Embree, D. G.; Smith, C. C. 1966. Distinguishing adults of the winter moth, 

Operophtera brumata (L.), and Bruce spanworm, Operophtera bruceata (Hulst) (Lepidoptera: Geometridae). Canadian Entomologist 98: 258-261.

# Fitzpatrick, S. M.; Troubridge, J. T.; Peterson, B. 1991. Distribution of European winter 

moth, Operophtera brumata (L.), and Bruce spanworm, O. bruceata (Hulst), in the lower 

Fraser Valley, British Columbia. Journal of Entomological Society of British Columbia 88: 

39-45.

# Underhill, E. W.; Millar, J. G.; Ring, R. A.; Wong, J. W.; Barton, D.; Giblin, M. 1987. Use 

of a sex attractant and an inhibitor for monitoring winter moth and Bruce spanworm populations. Journal of Chemical Ecology 13: 1319-1330.
78

Bruce Spanworm, Operophtera bruceata (Hulst)

Winter Moth, Operophtera brumata (L.)

Lepidoptera: Geometridae

Underhill, E. W.; Millar, J. G.; Ring, R. A.; Wong, J. W.; Barton, D.; Giblin, M. 1987. Use of 

a sex attractant and an inhibitor for monitoring winter moth and Bruce spanworm populations. Journal of Chemical Ecology 13: 1319-1330.

Objective

To test the addition of an inhibitor in pheromone traps for differentiating populations of O. 

bruceata and O. brumata.

Abstract

The winter moth, Operophtera brumata (Linnaeus), is a significant defoliator of northern red 

oak, Quercus rubra L., in Nova Scotia where it was introduced accidentally in 1930. The 

range of O. brumata has expanded to include most of eastern Canada, portions of the northeastern US and even western Canada. Adults of O. brumata and the related Bruce spanworm, Operophtera bruceata (Hulst), are similar morphologically. O. bruceata (Hulst), is a 

major defoliator of maple (Acer spp.) and aspen (Populus spp.) stands throughout Canada.

The sex pheromone of O. brumata, (Z,Z,Z)-1,3,6,9-nonadecatetraene (1,3Z,6Z,9Z-19:H), also 

attracts male O. bruceata. The two species are nearly identical in appearance and require 

examination of the genitalia for proper identification. Both species are pests and monitoring each population is of importance. The pheromone (1,3Z,6Z,9Z-19:H) and several of its 

analogues were evaluated in the laboratory and in field trials for male response. Traps baited 

with (1,3Z,6Z,9Z-19:H) captured males of both species, but the addition of (E,Z,Z)-1,3,6,6- 

nonadecatetraene (1,3E,6Z,9Z-19:H), inhibited the attraction of O. bruceata. The analogue 

1,3E,6Z,9Z-19:H alone was not attractive to either O. brumata or O. bruceata. The discovery 

of the inhibitor 1,3E,6Z,9Z-19:H facilitates the identification and separation of O. bruceata

and O. brumata populations, but traps baited with 1,3Z,6Z,9Z-19:H and 1,3E,6Z,9Z-19:H 

still capture a low percentage of O. bruceata. Dissection of male genitalia is necessary to 

detect low densities of O. bruceata captured in traps with the inhibitor.

Hara traps (Hara Products Ltd., Swift Current, Saskatchewan) were preferred over Pherocon 

1CP traps (Zoecon Corp., Palo Alto, CA) as trap saturation was a concern with the latter. 

Sampling Procedure

Install Hara traps baited with 100 μg each of 1,3Z,6Z,9Z-19:H and 1,3E,6Z,9Z-19:H and 
79

traps baited with 100 μg of 1,3Z,6Z,9Z-19:H alone at each site. Load compounds on rubber 

septa and include an insecticidal strip in each trap to kill captured males. Hang traps 1.0–1.5 

m above ground on trees or poles. Traps should be set 10–15 m from each other.

In areas predominated by O. bruceata, fewer males should be captured in traps with the 

inhibitor than in traps without the inhibitor. Trap captures should not differ in areas predominated by O. brumata, thus significant differences in trap captures should signal the presence 

of O. bruceata at the site. Further research may refine this technique to where O. bruceata

does not occur in traps with the inhibitor, but until then dissection of the genitalia is still 

necessary to detect low densities of O. bruceata in these traps. Dissection of males in traps 

baited with only 1,3Z,6Z,9Z-19:H is not necessary. 

Reference

Eidt, D. C.; Embree, D. G.; Smith, C. C. 1966. Distinguishing adults of the winter moth, 

Operophtera brumata (L.), and Bruce spanworm, Operophtera bruceata (Hulst) (Lepidoptera: Geometridae). Canadian Entomologist 98: 258-261.
80

Bruce Spanworm 

Operophtera bruceata (Hulst)

Lepidoptera: Geometridae

Hébert, C.; St. Antoine, L. 1999. Oviposition trap to sample eggs of Operophtera bruceata

(Lepidoptera: Geometridae) and other wingless geometrid species. Canadian Entomologist 

131: 557-565.

Objective

To develop an oviposition trap for O. bruceata that could be used in large scale population 

monitoring.

Abstract

Bruce spanworm, Operophtera bruceata (Hulst), is a major defoliator of maple (Acer spp.) 

and aspen (Populus spp.) stands throughout Canada. Previous sampling methods for O. 

bruceata include the use of sticky bands placed on the bole of trees to sample the wingless 

female, but these are costly and laborious to maintain.

Adults of O. bruceata and the related winter moth, Operophtera brumata (Linnaeus), are 

similar morphologically. Standard trapping techniques using sticky bands that catch adults 

require tedious work because the genitalia must be examined in the laboratory to differentiate between the two species. However, winter moth eggs are significantly smaller than 

Bruce spanworm eggs and this characteristic could easily be used to differentiate between the 

species. A new egg sampling method using an oviposition trap has been refined from earlier 

work (Hébert & St. Antoine 1998), where O. bruceata lay eggs on a polyurethane foam band 

covered by a Multi-Pher® plate. An accurate method of sub-sampling eggs on the polyurethane foam band was tested statistically and is recommended when egg densities on the traps 

are high. Density of eggs from 3 randomly selected sub-samples (x) (1 x 10 cm each) was 

related positively to density of the entire foam strip (y) (y = 6.7 + 10.5x, mean r2

 = 0.957). 

This trap is also useful at monitoring populations of fall cankerworm, Alsophila pometaria

(Harris).

Sampling Procedure

The standard trap is a 1.2 m post of black ABS (acrylonitrile butadiene styrene) pipe, with a 

10 x 31 cm band of polyurethane foam attached lengthwise around the top of the post. Each 

trap has a model I Multi-Pher trap with a closed funnel placed on top of the post. Posts are 

sandblasted to ensure that the wingless females can climb to the oviposition strip. Trap 

placement and density will depend on the objective(s) of the sampling effort. Traps should 
81

be installed before the oviposition period of O. bruceata and collected after oviposition has 

ceased. Examine collected foam strips and count the number of eggs laid on them with the 

aid of a light table. Sub-sampling the foam strips is often necessary to reduce effort when 

moth populations are high. Divide the foam strip into 30 pieces, measuring 1 x 10 cm each. 

Randomly select and count the eggs on three pieces, which is the recommended sub-sample 

size.

Notes

Several wingless geometrids could lay eggs on the foam strips, causing a bias in estimation 

of the population of the targeted insect pest. An insecticidal strip in the Multi-Pher trap to 

trap adult females might help determine the relative contribution of eggs by species. The authors also suggest using egg size to differentiate between O. brumata and O. bruceata, which 

has smaller eggs, but caution that this technique needs to be tested empirically with the oviposition trap (see Cuming 1961 and Brown 1962 regarding egg sizes of these two species). 

References

Brown, C. E. 1962. The life history and dispersal of the Bruce spanworm, Operophtera bruceata (Hulst) (Lepidoptera: Geometridae). Canadian Entomologist 94: 1103-1107.

Cuming, F. G. 1961. The distribution, life history, and economic importance of the winter 

moth, Operophtera brumata (L.) (Lepidoptera:Geometridae) in Nova Scotia. Canadian 

Entomologist 93: 135-142.

* Hébert, C.; St. Antoine, L. 1998. The oviposition trap: a new technique for sampling eggs 

of the Bruce spanworm and similar species. Res. Notes 5. Canadian Forest Service, Laurentian Forestry Centre; 4 p.
82

Winter Moth

Operophtera brumata (L.)

Lepidoptera: Geometridae

Hébert, C.; St. Antoine, L. 1999. Oviposition trap to sample eggs of Operophtera bruceata

(Lepidoptera: Geometridae) and other wingless geometrid species. Canadian Entomologist 

131: 557-565.

Objective

To develop an oviposition trap for O. brumata that could be used in large scale population 

monitoring.

Abstract

Winter moth, Operophtera brumata (Linnaeus), is a significant defoliator of northern red oak, 

Quercus rubra L., in Nova Scotia where it was introduced accidentally from Europe in 1930. 

The range of O. brumata has expanded to include most of eastern Canada, portions of the 

northeastern US and even western Canada. Although O. brumata is generally controlled by 

the parasitoids Cyzenis albicans (Faller) and Agrypon flaveolatum (Grav.) in its eastern range, 

populations of this pest may still reach epidemic levels and require sampling to determine if 

control measures are needed.

Adults of O. brumata and the related Bruce spanworm, Operophtera bruceata (Hulst), are 

similar morphologically. Standard trapping techniques using sticky bands that catch adults 

require tedious work because the genitalia must be examined in the laboratory to differentiate between the two species. However, winter moth eggs are significantly smaller than 

Bruce spanworm eggs and this characteristic could easily be used to differentiate between the 

species. A new egg sampling method using an oviposition trap has been refined from earlier 

work (Hébert & St. Antoine 1998), where O. brumata females lay eggs on a polyurethane 

foam band covered by a Multi-Pher® plate. Sub-sampling eggs on the polyurethane foam 

band is recommended when egg densities on the traps are high. Because O. brumata and O. 

bruceata have a similar life history and oviposition behavior as fall cankerworm, Alsophila 

pometaria (Harris), the authors suggest this trapping technique should work for all three 

geometrid species.

Sampling Procedure

The standard trap is a 1.2 m post of black ABS (acrylonitrile butadiene styrene) pipe, with a 

10 x 31 cm band of polyurethane foam attached lengthwise around the top of the post. Each 

trap has a model I Multi-Pher trap with a closed funnel placed on top of the post. Posts are 
83

sandblasted to ensure that the wingless females can climb to the oviposition strip. Trap 

placement and density will depend on the objective(s) of the sampling effort. Traps should 

be installed before the oviposition period of O. brumata and collected after oviposition has 

ceased. Examine collected foam strips and count the number of eggs laid on them with the 

aid of a light table. Sub-sampling the foam strips is often necessary to reduce effort when 

moth populations are high. Divide the foam strip into 30 pieces measuring 1 x 10 cm each. 

Randomly select and count the eggs on three pieces, which is the recommended sub-sample 

size.

Notes

Several wingless geometrids could lay eggs on the foam strips, causing a bias in estimation 

of the population of the targeted insect pest. An insecticidal strip in the Multi-Pher trap to 

trap adult females might help determine the relative contribution of eggs by species. The authors also suggest using egg size to differentiate between O. brumata and O. bruceata, which 

has smaller eggs, but caution that this technique needs to be tested empirically with the oviposition trap (see Cuming 1961 and Brown 1962 regarding egg sizes of these two species). 

References

Brown, C. E. 1962. The life history and dispersal of the Bruce spanworm, Operophtera bruceata (Hulst) (Lepidoptera: Geometridae). Canadian Entomologist 94: 1103-1107.

Cuming, F. G. 1961. The distribution, life history, and economic importance of the winter 

moth, Operophtera brumata (L.) (Lepidoptera:Geometridae) in Nova Scotia. Canadian Entomologist 93: 135-142.

* Hébert, C.; St. Antoine, L. 1998. The oviposition trap: a new technique for sampling eggs 

of the Bruce spanworm and similar species. Res. Notes 5. Canadian Forest Service, Laurentian Forestry Centre; 4 p. 
84

Winter Moth

Operophtera brumata (L.)

Lepidoptera: Geometridae

Reeks, W. A. 1956. Sequential sampling for larvae of the winter moth, Operophtera brumata

(Linn.) (Lepidoptera: Geometridae). Canadian Entomologist 88: 241-246.

Objective

To develop a sequential sampling plan for fourth instars of O. brumata.

Abstract

Winter moth, Operophtera brumata (L.), is a significant defoliator of northern red oak, Quercus rubra L., in Nova Scotia where it was introduced accidentally in 1930. The range of O. 

brumata has expanded to include most of eastern Canada, portions of the northeastern US 

and western Canada. Severe infestations result in thinned foliage, branch dieback, and tree 

mortality.

A sequential sampling plan was developed for fourth instar O. brumata on red oak, Quercus 

rubra L., in Nova Scotia. Twelve clusters of leaves randomly sampled from the live crown 

of eight trees each was shown to be an adequate sample size to predict defoliation by O. brumata. An additional two trees can be sampled if the cumulative tally of O. brumata larvae 

from eight trees remains in the “continue sampling” zones indicated in Table II. Light, moderate and severe infestations corresponded with approximate defoliation levels of 0–25%, 

35–80% and 90–100% on individual trees, respectively.

Sampling Procedure

Sample when fourth instar O. brumata are present, typically during the first two weeks of June 

in Nova Scotia. Randomly select eight red oaks within an area of concern. Remove 12 clusters 

of leaves from each tree from any part of the crown. A leaf cluster is defined as all the leaves 

on a current shoot. Examine the first cluster of leaves and tally the number of fourth instar O. 

brumata present. Reference Table II. If the tally falls within the light-moderate or moderatesevere zones, examine the next cluster of leaves for larvae and continue to reference Table II. 

Stop sampling when the cumulative count falls into a light, moderate, or severe zone. If all 

eight clusters are examined and the cumulative tally of larvae remains in the “continue sampling” columns, randomly select two more oaks and sample as above. Classify the infestation 

as “light-moderate” or “moderate-severe” as appropriate if no decision is reached after sampling 10 trees. Light, moderate and severe infestations correspond to approximate defoliation 

levels of 0–25%, 35–80% and 90–100% on a per tree basis, respectively.
85

Notes

This sequential plan may also be useful for fall cankerworm, Alsophila pometaria (Harris), 

which has a similar distribution and life history as O. brumata. Use this plan with caution for 

A. pometaria until it is validated for this species

Table II. Cumulative number of winter moth larvae per tree-sample (12 leaf clusters per treesample) for one to ten sample trees. Based on values for d as shown in text.

Tree

Cumulative number of larvae per tree-sample

Light-moderate zone, 

continue sampling

Moderate-severe zone, continue sampling

1

Light 

Zone

3–23

Moderate 

Zone

24–54

Severe

Zone

2 17–37 63–93

3 30–51 102–132

4 44–64 141–171

5 57–78 180–210

6 70–91 219–248

7 84–105 258–287

8 97–118 297–325

9 111–132 336–365

10 124–145 375–404

Table II is reprinted as fair use under 17 USC § 107.
86

Forest Tent Caterpillar

Malacosoma disstria (Hübner)

Lepidoptera: Lasiocampidae

Schmidt, B. C.; Roland, J.; Wakarchuk, D. 2003. Evaluation of synthetic pheromones for 

monitoring forest tent caterpillar (Lepidoptera: Lasiocampidae) populations. Environmental Entomology 32: 214-219.

Objective

To develop a synthetic pheromone for use in detecting and monitoring populations of M. disstria.

Abstract

Forest tent caterpillar, Malacosoma disstria Hübner, is a major defoliator of hardwood 

forests, particularly trembling aspen, Populus tremuloides Michx., in the northern USA and 

Canada. Young larvae feed on developing buds, while later instars feed gregariously, often 

defoliating the tree completely. Defoliation causes growth loss, twig dieback, and tree mortality in cases of prolonged infestation.

Three blends of pheromones and 5 volumes of a specific tent caterpillar blend were tested 

on epidemic and endemic populations of M. disstria. A three component blend of (Z,E)-

5,7-dodecadienal:(Z,Z)-5,7-dodecadienal:(Z)-7-dodecanal (100:1:10) was most attractive of 

the three blends tested. The highest volume of pheromone tested (390 μg) caught the most 

moths with fewest zero captures at a range of moth densities, though 100 μg per lure should 

be sufficient for use in a trapping program.

Sampling Procedure

Wing traps (Wing Trap I, Phero Tech Inc., Delta, B.C, Canada) are used to house the pheromone blend. Place a Flex Lure baited with 390 μg of (Z,E)-5,7-dodecadienal:(Z,Z)-5,7-

dodecadienal:(Z)-7-dodecanal (100:1:10) pheromone into each trap. For more information 

regarding the manufacturing methods of these lures, consult Canadian patent #2,218,157 and 

US patent #5,750,129. Install traps in field just prior to moth flight. Lures are effective for 

approximately 28 days under field conditions, which is adequate to span the flight period of 

M. disstria in Alberta. Place traps approximately 1.5 to 2.0 m above ground and about 100 m 

apart along transects in aspen stands.

Note

Optimal trap density was not provided in this article. Users should consult the authors or 

Phero Tech Inc. regarding updates to this protocol.
87

Gypsy Moth

Lymantria dispar (L.)

Lepidoptera: Lymantriidae

Liebhold, A.; Twardus, D.; Buonaccorsi, J. 1991. Evaluation of the timed-walk method of estimating gypsy moth (Lepidoptera: Lymantriidae) egg mass densities. Journal of Economic 

Entomology 84: 1774-1781.

Objective

To compare the timed-walk method of estimating egg mass densities of L. dispar against 

estimates in fixed-radius plots.

Abstract

Gypsy moth, Lymantria dispar (L.) is an important defoliator of numerous hardwoods. Defoliation reduces tree growth and vigor, and, in combination with other stress factors, can cause 

excessive tree mortality. Populations are spreading from an initial accidental introduction at 

Medford, MA, around 1868, throughout northeastern USA and neighboring Canada. 

Population monitoring of L. dispar would benefit from sampling methods requiring limited 

labor and time. Eggen and Abrahamson (1983) recommended using a timed-walk method 

of estimating egg mass densities, where all visible egg masses are tallied on a 5-minute walk 

through a plot. Egg mass densities are then predicted from the timed observations using linear regression. This technique has been criticized for several problems, including unspecified 

criteria for classifying population levels of L. dispar, unaddressed observer bias, and limited 

data collection for the development of the regression models used with this technique (Ravlin 

et al. 1987). 

A study was conducted in the Mid-Atlantic region of the USA to compare the statistical 

feasibility of a timed-walk method (Eggen and Abrahamson 1983) against a sampling method 

using a fixed-radius plot (Wilson and Fontaine 1978). A single fixed-radius plot provided a 

more precise estimate of L. dispar egg mass densities than conducting ≤ four 5-min. walks 

when populations were at <1,500 egg masses per 0.40 hectare, which would encompass the 

thresholds for control decisions in most management programs for L. dispar. Both methods 

took ≈20 min. to conduct. The greater precision offered by using 0.01 ha fixed-radius plots 

offsets the effort needed to set up the plots and sample them.

Sampling Procedure

Use sequential sampling plans developed for estimating L. dispar egg mass densities in fixed-
88

radius plots with known precision levels (Fleischer et al. 1991, 1992; Kolodny-Hirsch 1986). 

If using timed walks to sample L. dispar, each observer should be calibrated to account for 

observational bias, spatial variation in egg mass densities, and differences in terrain across 

the landscape. This can be done by first estimating egg mass densities with a more precise 

fixed-radius or variable-radius plot sampling procedure, but the effort would be greater than 

simply using a fixed-radius plot in the first place.

Note

See the original publication for a complete description of the statistics used to compare the 

timed-walk and fixed-radius plot sampling methods.

References

Eggen, D.; Abrahamson, L. P. 1983. Estimating gypsy moth egg mass densities. Misc. Publ. 

No. 1, ESF 83-002. Albany, NY: State University of New York, College of Environmental 

Science and Forestry; 28 p. 

* Fleischer, S. J.; Ravlin, F. W.; Reardon, R. C. 1991. Implementation of sequential sampling 

plans for gyspy moth (Lepidoptera: Lymantriidae) egg masses in eastern hardwood forests. 

Journal of Economic Entomology 84: 1100-1107.

* Fleischer, S. J.; Carter, J.; Reardon, R.; Ravlin, F. W. 1992. Sequential sampling plans for 

estimating gypsy moth egg mass density. NA-TP-07-92. Morgantown, WV: U.S. Department of Agriculture, Forest Service, Northeastern Area; 12 p.

* Kolodny-Hirsch, D. M. 1986. Evaluation of methods for sampling gypsy moth (Lepidoptera: Lymantriidae) egg mass populations and development of sequential sampling 

plans. Environmental Entomology 15: 122-127.

Ravlin, F. W.; Bellinger, R. G.; Roberts, E. A. 1987. Gypsy moth management programs in 

the United States: status, evaluation and recommendations. Bulletin of Entomological 

Society of America 33: 90-98.

* Wilson, R. W. Jr.; Fontaine, G. A. 1978. Gypsy moth egg mass sampling with fixed and variable radius plots. Agric. Handb. 523. Washington, DC: U.S. Department of Agriculture; 46 p.
89

Gypsy Moth

Lymantria dispar (L.)

Lepidoptera: Lymantriidae

Sharov, A. A.; Liebhold, A. M.; Roberts, E. A. 1997. Methods for monitoring the spread of 

gypsy moth (Lepidoptera: Lymantriidae) populations in the Appalachian Mountains. Journal of Economic Entomology 90: 1259-1266.

Objectives

To compare variability of population boundaries of L. dispar in time and space as estimated 

from pheromone trap data and egg mass counts; to select the least variable threshold for use 

in monitoring population spread; to evaluate spread rates estimated from male moth trap 

catches; and to assess the relationship between accuracy and placement of pheromone traps.

Abstract

Gypsy moth, Lymantria dispar (L.) is an important defoliator of numerous hardwoods. Defoliation reduces tree growth and vigor, and, in combination with other stress factors, can cause 

extensive tree mortality. Populations are spreading from an initial accidental introduction at 

Medford, MA, around 1868, throughout northeastern USA and neighboring Canada. Population spread must be monitored to evaluate control strategies in gypsy moth management, 

including appropriate placement and timing of control measures, identification of areas for 

quarantine status, and targeting areas for treatment rather than broadcasting pesticides over 

widespread areas. Suppression or eradication of detected colonies ahead of the advancing 

front is an important tool in reducing the rate of spread. A model developed by Sharov and 

Liebhold (1997) predicts that control of isolated colonies will reduce the annual spread of L. 

dispar by 53%.

Historical pheromone trap data (1988-1995) and egg mass count data (1988-1991) from the 

Appalachian Mountains in northern Virginia and southern West Virginia were analyzed by 

the authors. Regression analysis of population boundaries in time and space, as measured by 

trap data and egg mass counts, was used to calculate spread rates from year to year. Sampling methods used to collect pheromone trap data and egg mass densities are described by 

Sharov and others (1995, 1996). Thresholds of 1, 3, 10, 30, 100, and 300 male moths per 

pheromone trap and 1, 3, 10, and 30 egg masses per 0.01 ha were evaluated. Correlograms 

for male moth trap catches were developed to analyze autocorrelation of spread rates in 

space, time, and among population thresholds. The effect of intertrap distance on population 

spread rate was estimated using a modified Tukey jackknife method (refer to original publication for greater detail of statistical analyses).
90

Sampling Procedure

Pheromone traps: A 2.5-km grid is an appropriate intertrap distance with a relatively high accuracy when estimating annual spread rates (SE = 13–21% of the annual spread rate). Intertrap distances can be maximized to 11-km, but the authors recommend increasing the distance to no more than 8 km in case traps are lost after deployment, thus creating gaps in the 

data. Increasing the intertrap distance from 2.5 to 7 km raised the mean standard error 3.9 

times. Use a threshold of 10 male moths per trap for control decisions. Low trap catches (<3 

males) likely result from wind-borne male migrants, while higher trap catches likely reflect 

reproduction by local colonies.

Egg mass surveys: Use a threshold of 10 egg masses per 0.01 ha, the least variable threshold 

tested, for control decisions.

Notes

Population boundaries are based on best-cell classification as described by Sharov et al.

(1995, 1996). The authors also examined using defoliation maps as a means to estimate 

spread of L. dispar, but found that boundaries were highly variable and did not accurately reflect annual spread. The authors caution that the Appalachian Mountains represent a unique 

geographical area and their research may not accurately reflect population spread in other 

regions where gypsy moth is established.

References

Sharov, A. A., Roberts, E. A.; Liebhold, A. M.; Ravlin, F. W. 1995. Gypsy moth (Lepidoptera: 

Lymantriidae) spread in the central Appalachians: three methods for species boundary 

estimation. Environmental Entomology 24: 1529-1538.

Sharov, A. A.; Liebhold, A. M.; Roberts, E. A. 1996. Spread of gypsy moth (Lepidoptera: 

Lymantriidae) in the central Appalachians: comparison of population boundaries 

obtained from male moth capture, egg mass counts and defoliation records. 

Environmental Entomology 25: 783-792.

Sharov, A. A.; Liebhold, A. M. 1998. Model of slowing the spread of gypsy moth 

(Lepidoptera: Lymantriidae) with a barrier zone. Ecological Applications 8: 1170-

1179. 
91

Douglas Fir Tussock Moth

 

Orgyia pseudotsugata (McDonnough)

Lepidoptera: Lymantriidae

Dahlsten, D. L.; Rowney, D. L.; Copper, W. A.; Wenz, J. M. 1992. Comparison of artificial 

pupation shelters and other monitoring methods for endemic populations of Douglas-fir 

tussock moth, Orgyia pseudotsugata (McDunnough) (Lepidoptera: Lymantriidae). Canadian Entomologist 124: 359-369.

Objective

To predict larval populations of O. pseudotsugata on white fir in northern California from 

counts of cocoons or egg masses found in artificial pupation shelters.

Abstract

Douglas-fir tussock moth, Orgyia pseudotsugata (McDonnough), is a periodic defoliator 

of Douglas-fir, Pseudotsuga menziesii (Mirb.), and true firs, Abies spp., in western North 

America. Outbreaks occur quite unexpectedly every 7–10 years and usually persist for 3–4 

years. Defoliation by O. pseudotsugata can be severe and cause widespread tree mortality 

during the first year of an outbreak. Surviving trees may exhibit growth loss, top-kill, and 

tree deformity.

Artificial pupation shelters as a means of monitoring populations of O. pseudotsugata in 

northern California were compared to sampling larval populations from three crown levels 

(as described in Dahlsten et al. 1985) and the use of pheromone traps. Cocoon numbers from 

the shelters were positively correlated with larval density in the same year in 1978 (r = 0.76) 

and 1979 (r = 0.71). Egg mass counts were not strongly correlated with larval density the 

following year (r = 0.55), but the positive correlation indicates that egg mass counts can be 

used to predict larval density. Furthermore, egg mass counts from artificial pupation shelters 

require less effort than sampling larval densities from three crown levels from multiple trees, 

and the shelters provide an earlier prediction of larval populations than foliage sampling allows. Artificial pupation shelters also offer an advantage over pheromone traps in that they 

do not require regular service and can be left in place without service until counts are made. 

Population increases detected from cocoon or egg mass counts the previous fall could be 

confirmed by sampling larval densities on foliage. 

Sampling Procedure

Make pupation shelters from wood blocks 9 cm tall, 10 cm wide, and 4 cm deep (Fig. 1). Cut 

an opening 2.5 cm in diameter and approximately 4 cm deep on one side of the block, near 
92

the bottom. Larvae will pupate inside this opening. Cut a 2.5 cm hole through the block near 

the top of the wide face. Hang the shelter on the tree using this hole.

Establish 0.4 ha plots containing white fir, Abies concolor (Gord. and Glend.) Lindl., within 

the area to be monitored. In each plot, install a pupation shelter on 10 white firs before larvae 

begin pupating in late July. Leave shelters in place until adults emerge if counting cocoons, 

or until egg-laying has ceased if counting egg masses. Using the artificial pupation shelters 

for multiple, consecutive years will allow land managers to monitor trends in O. pseudotsugata populations over time. 

Notes

Paper cartons, as shown in Fig. 1, compared favorably to the wooden pupation shelters as a 

means of monitoring populations of O. pseudotsugata. However, the wooden shelters are 

more durable and can be left in place for several years. Cleaning the pupation shelters after 

taking counts will avoid the need to distinguish between old cocoons or egg masses from 

previous years and those from the current season.

The authors noted that using a larger number of plots within an area may reduce variation 

seen among plots, and installing more shelters per plot may improve the prediction of population trends when populations are very low. Further work is needed to correlate defoliation 

within plots to cocoon or egg mass counts. 

Reference

Dahlsten, D. L.; Norick, N. X.; Wenz, J. M.; Williams, C. B.; Rowney, D. L. 1985. The dynamics of Douglas-fir tussock moth populations at low levels at chronically infested sites 

in California. In: Bevan, D.; Stokley, J. T., editors. Site characteristics and population dynamics of lepidopteran and hymenopteran forest pests: Proceedings of IUFRO Conference 

Subject Group S2.07.06; 1980 September 1–7; Dornoch, Scotland; p. 132–139.
93

Figure 1. Artificial pupation shelters used for monitoring Douglas-fir tussock moth cocoons and egg 

masses in California, 1978 nad 1979. Refer to the text for more information regarding shelter design.

Modified Figure 1 reprinted with permission from the authors, granted April 21, 2009.
94

Douglas Fir Tussock Moth

Orgyia pseudotsugata (McDonnough)

Lepidoptera: Lymantriidae

Daterman, G. E.; Wenz, J. M.; Sheehan, K. A. 2004. Early warning system for Douglas-fir 

tussock moth outbreaks in the western United States. Western Journal of Applied Forestry 

19: 232-241.

Objective

To monitor increasing densities of O. pseudotsugata using a system of pheromone-baited 

traps in sentinel plots throughout the western USA.

Abstract

Douglas-fir tussock moth, Orgyia pseudotsugata (McDonnough), is a periodic defoliator 

of Douglas-fir, Pseudotsuga menziesii (Mirb.), and true firs, Abies spp., in western North 

America. Outbreaks occur quite unexpectedly every 7–10 years and usually persist for 3–4 

years. Defoliation by O. pseudotsugata can be severe and cause widespread tree mortality 

during the first year of an outbreak. Surviving trees may exhibit growth loss, top-kill, and 

tree deformity.

Beginning in 1979, an Early Warning System established in the western USA has allowed 

the timely detection of increasing populations of O. pseudotsugata with pheromone-baited 

traps. Trapping in sentinel plots can detect increasing populations of O. pseudotsugata 1–3 

years before defoliation is visible to aerial surveys. Each sentinel plot of host trees contains 

a line of 5 pheromone traps spaced at 22.9 m intervals. Traps are deployed annually from approximately late July to early November. Catches of ≥25 moths in each trap within a sentinel 

plot indicate increasing populations of O. pseudotsugata with the potential to produce visible 

defoliation within 1–3 years. Given the variation occurring among traps within plots and 

among plots, land managers can use the operational threshold of an average of 17 moths in 

each of the 5 traps installed in a sentinel plot. Populations exceeding this threshold require 

follow-up ground surveying for egg masses or larvae to delineate the infested area and confirm population levels. Based on trap catches from this early warning system, land managers 

can decide whether chemical control measures are necessary for O. pseudotsugata in the following spring with sufficient time to plan control operations over the winter months. 

Sampling Procedure

Establish at least one sentinel plot per 1,214 ha. Plots should be spaced evenly throughout 

the area of concern and should reflect any previous defoliation. Consider establishing addi-
95

tional plots in areas with greater value or where management objectives may include applying control measures very quickly when populations exceed the threshold.

Traps consist of a 1.9-liter milk carton modified to a delta trap with an interior coated with an 

adhesive. Suspend a pellet of synthetic pheromone above the adhesive on a long pin. Refer 

to Daterman et al. (1979) for additional details regarding the pheromone traps used in the 

early warning system. Install traps each year in sentinel plots between late July and mid-August. Remove traps between mid-October and early November. Tally the number of moths 

in each trap. 

Catches of ≥25 moths per trap signal increasing populations of O. pseudotsugata with the potential to produce visible defoliation in the next 1–3 years. However, mean moth catches will 

naturally vary among traps within a plot, among plots, and across regions. Operationally, 

land managers should consider control measures when trap catches average 17 moths per trap 

within a sentinel plot to account for a variation of ±30% trap catch. When O. pseudotsugata

populations exceed this threshold, use ground surveys to pinpoint the location of the infestation and confirm rising populations by sampling egg masses or larvae. Survey egg masses, 

pupae, and/or cocoons in the fall of the year rising populations are detected or young larvae 

in the following spring. Surveys should be conducted in the 1- to 2-km area surrounding the 

plot where increasing populations were detected as pheromone traps may attract O. pseudotsugata migrating from other areas. Additional sentinel plots can be established in areas 

where rising populations are detected to confirm the population increase, but ground surveys 

are strongly encouraged so that control measures can be implemented as soon as possible if 

needed. Waiting until the end of the second year of rising populations to make management 

decisions may cost land managers the benefit of early warning in those cases where defoliation occurs within 1 year of initial warning.

Reference

Daterman, G. E.; Livingston, R. L.; Wenz, J. M.; Sower, L. L. 1979. How to use pheromone 

traps to determine outbreak potential. Douglas-fir Tussock Moth Handb. No. 546. 

Washington, D.C.: U.S. Department of Agriculture, Forest Service; 11 p.
96

Douglas Fir Tussock Moth

Orgyia pseudotsugata (McDonnough)

Lepidoptera: Lymantriidae

Mason, R. R.; Overton, W. S. 1983. Predicting size and change in nonoutbreak populations of 

the Douglas-fir tussock moth (Lepidoptera: Lymantriidae). Environmental Entomology 12: 

799-803.

Objective

To predict future population trends of O. pseudotsugata populations from larval densities currently in a nonoutbreak phase.

Abstract

Douglas-fir tussock moth, Orgyia pseudotsugata (McDonnough), is a periodic defoliator of 

Douglas-fir, Pseudotsuga menziesii (Mirb.), and true firs, Abies spp., in western North America. Outbreaks occur quite unexpectedly every 7–10 years and usually persist for 3–4 years. 

Large numbers of trees are often defoliated before direct control measures can be applied. 

Defoliation by O. pseudotsugata can be severe and cause tree mortality during the first year 

of an outbreak, and surviving trees often exhibit growth loss and top kill.

Data collected from 11 generations of O. pseudotsugata from four widely separated populations in western USA were used to develop equations for predicting changes in nonoutbreak 

populations. The density of larvae surviving to late instars (X) in a given generation was a 

good predictor of the early larval density in the subsequent generation (Y), expressed as logY 

= 0.950 + 0.963 logX (r2

 = 0.85). Furthermore, densities of early and late instars can be used 

to detect increasing population densities in advance of an actual outbreak. High densities 

of surviving larvae are necessary for population increase in O. pseudotsugata, but population decline may be related to mortality in a different life stage than larvae. Overall, these 

procedures provide valuable information needed for management decisions with time to plan 

control operations in advance.

Sampling Procedure

Initiate sampling of early larvae (instars 1 and 2) when the density O. pseudotsugata appears 

to be approaching an outbreak phase but still fewer than 20 early larvae are present per 0.65 

m2

 of branch foliage. Sample larvae in midcrown foliage using the methods of Mason (1970, 

1977, 1979). Express larval densities as the mean number of larvae per 0.65 m2 of branch 

foliage. Sample lower crown foliage if densities are below 1 larva per 0.65 m2

 of branch foliage. Return to plots and sample late larvae (instars 5 and 6) when present.
97

Use the following equation to predict the general population density of early instars in 

the subsequent generation:

logY = 0.950 + 0.963 logX

where Y = the density of early instars in a given generation and X = the density of late instars 

in the previous generation. 

Use the following equation to predict changes in the size of the subsequent generation:

Y = —0.214 + 13.825X

where Y = the trend index (I) and X = larval survival (S), or the proportion of larvae in a single generation surviving to late instars. The trend index, I, is the ratio of early instar densities 

in two successive generations (I = Nt+1/Nt

 where Nt = the density of early instars in a given 

generation and Nt+1 = the density of surviving late instars in the following generation). Populations are classified as increasing when I >1.0, but this regression model does not adequately 

represent declining populations where I <1.0. 

Larval survival (S) is defined as S = Ns

/ Nt, where Ns = the density of surviving late instars in 

the same generation as Nt

. Larval survival can be used to detect declining or static populations not represented by the trend index in the above model. A larval survival rate of >0.2 

generally signals a rapidly increasing population whereas a rate of <0.2 indicates that the 

population will remain static or decline.

Notes

These procedures were developed from data collected from low-density populations of O. 

pseudotsugata that had not erupted into an outbreak recently or were only in the very beginning of an outbreak phase. The equations may not be valid for larval densities during an 

outbreak or in a post-outbreak decline.

References

* Mason, R. R. 1970. Development of sampling methods for the Douglas-fir tussock moth, 

Hemerocampa pseudotsugata (Lepidoptera: Lymantriidae). Canadian Entomologist 102: 

836-845.

* Mason, R. R. 1977. Sampling low-density populations of the Douglas-fir tussock moth by 

frequency of occurrence in the lower tree crown. Res. Pap. PNW-216. Portland, OR: U.S. 

Department of Agriculture, Forest Service, Pacific Northwest Research Station; 8 p.

* Mason, R. R. 1979. How to sample Douglas-fir tussock moth larvae. Agric. Handb. 547. 

Washington, D.C.: U.S. Department of Agriculture, Forest Service; 15 p.
98

Douglas Fir Tussock Moth, Orgyia pseudotsugata (McDonnough)

Western Spruce Budworm, Choristoneura occidentalis (Freeman)

Lepidoptera: Lymantriidae

Mason, R. R.; Paul, H. G. 1994. Monitoring larval populations of the Douglas-fir tussock 

moth and the western spruce budworm on permanent plots: sampling methods and statistical properties of data. Gen. Tech. Rep. PNW-GTR-333. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacific Northwest Research Station; 23 p.

Objective

To describe sampling methods for determining larval densities of O. pseudotsugata and C. 

occidentalis.

Abstract

Douglas-fir tussock moth, Orgyia pseudotsugata (McDonnough), is a periodic defoliator 

of Douglas-fir, Pseudotsuga menziesii (Mirb.), and true firs, Abies spp., in western North 

America. Outbreaks occur quite unexpectedly every 7–10 years and usually persist for 3–4 

years. Defoliation by O. pseudotsugata can be severe and cause widespread tree mortality 

during the first year of an outbreak. Surviving trees may exhibit growth loss, top-kill, and 

tree deformity. Western spruce budworm, Choristoneura occidentalis Freeman, also attacks 

Englemann spruce, Picea englemannii Parry ex. Englem., and larch, Larix occidentalis Nutt., 

in addition to Douglas-fir and true firs in western North America. Infestation of C. occidentalis in mature stands can result in growth loss, top kill, and occasional tree mortality.

This work is an extensive summary of earlier published studies and is derived from research 

conducted in eastern Oregon and Washington. The authors demonstrate that permanent plots 

in a geographical monitoring unit offer a statistically reliable tool for monitoring densities of O. 

pseudotsugata and C. occidentalis. Typically, larval population densities are estimated by sampling a number of tree clusters in a small area and a number of small areas in a stand. The midcrown of Douglas-fir <15 m tall is the preferred region for sampling branches. Traditionally three 

45-cm branch tips are sampled per tree on each of 20 to 50 trees in a plot, depending on larval 

densities (low and high populations requiring a larger number of samples). Sampling should be 

timed to coincide with presence of first and second instars of O. pseudotsugata and nominal fourth 

instars of C. occidentalis. Alternatively, branches in the lower crown can be sampled and the resulting estimates of larval density per sampling unit converted to mid-crown density per m2 using 

a corrective equation. Equations are presented to estimate the number of plots needed given the 

size of the area of concern. Roughly 50 plots would be needed to estimate tussock moth density 

at the size of a state or Canadian province. Sampling methods must be consistent across regions 

and time to build a meaningful database monitoring population trends.
99

Sampling Procedure

Permanent sample plots are generally about 2 ha in size. Plots should be selected to adequately reflect the larger monitoring unit, whether the unit is a ranger district or a national 

forest. Each sample plot should contain a cluster of Douglas-fir and true fir trees. Trees 

should have canopies accessible from the ground. Randomly select trees to reflect the species composition within plots. Plots should be sampled annually to monitor population 

trends over time and regions.

Begin sampling after budburst when new shoots are ≤4–6 cm long. Sample the same instars 

each year. Typically early instars (i.e., first and second instars) are preferred for O. pseudotsugata, while nominal fourth instars (i.e., instars III, IV, and V) are preferred for C. occidentalis. Sample three 45-cm branch tips from the lower crown of each tree using a beat sheet 

and a beat stick. Be aware that C. occidentalis larvae on webbed shoots may require more 

vigorous beating to dislodge them than O. pseudotsugata larve. Sampling 20–50 trees in a 

plot provides a density estimate with a standard error of <20% of the mean at the 68% probability level. Lower pest densities require a larger sample size than higher pest densities at 

the same precision level. Examine arthropods dislodged onto the beat sheet and record the 

number of O. pseudotsugata and C. occidentalis larvae found separately for each 3-branch 

sample. 

Calculate the mean number of larvae for each species per sampling unit, 

 

y, using the following formula:

 

M = 1

n

Mi

i = 1

n

∑

where yi = the number of larvae in the sampling unit of the ith tree and m = the number of 

trees sampled per plot. Sample variance among the 3-branch sampling units, V(y), can be 

calculated for each cluster of trees using the following formula, with yi

 and m described as 

above:

 

V M( )= 1

n −1 ( ) Mi − M

i = 1

n

∑ 2

Calculate the mean population density for a monitoring unit (M) using the following equation:

 

M = 1

n

Mi

i = 1

n

∑
100

where Mi

 = the larval density in the ith sampling plot of the unit and n = the number of plots 

per unit. The interplot variance, V(M), can be calculated among the sampling plots using the 

following formula, with Mi and n described as above:

 

V M( )= 1

n −1 ( ) Mi − M

i = 1

n

∑

2

Calculate the appropriate number of permanent sampling plots required by solving for interplot variance (V) in the following equation:

 

V M( )= a M

b

where a and b = parameters reflecting the spatial variation of larval populations and = the 

population mean. Substitute the value of V in the following equation:

 

n = t

2

V M( )

E2

where t = Student’s t for a specified confidence probability and E = the desired standard error 

of estimate to determine the number of permanent plots required within a monitoring unit. 

Both O. pseudotsugata and C. occidentalis larvae can be monitored simultaneously on the 

same plots within a management unit. District-sized, forest-sized, and province-sized units 

will generally need 15–20, 25–30, and 50–60 units, respectively. These numbers of plots 

should be sufficient for a precision of estimate of 20 percent of the mean and a confidence 

level of 68%.

Larval densities estimated from the lower crown should be converted to a standard 

abundance index in the mid-crown (M) using the following equation:

 

SA =∑PDi / n

where 3.1 = the average number of 3-branch sampling units per m2, 

 

y = the mean density 

of larvae per sampling unit, and R = the correction factor, which is a ratio of mid-crown to 

lower crown density of larvae. Substitute 2.0 as R for early instar O. pseudotsugata and 2.23 

for nominal fourth instar C. occidentalis to convert estimates of lower crown density per 

sampling unit to mid-crown density per m2.
101

Notes

Often early instar O. pseudotsugata and nominal fourth instar C. occidentalis can be sampled 

together at the same time, but not always. The two species should be sampled separately if 

C. occidentalis larvae become active in the spring weeks before O. pseudotsugata due to cool 

spring weather. 

Refer to the original publication for greater details regarding the calculations summarized 

here and additional information about monitoring permanent plots for O. pseudotsugata and 

C. occidentalis larvae.
102

Douglas Fir Tussock Moth 

Orgyia pseudotsugata (McDonnough)

Lepidoptera: Lymantriidae

Mason, R. R.; Wickman, B. E. 1991. Integrated pest management of the Douglas-fir tussock 

moth. Forest Ecology and Management 39: 119-130.

Objective

To review the integrated pest management practices for O. pseudotsugata populations.

Abstract

Douglas-fir tussock moth, Orgyia pseudotsugata (McDonnough), is a periodic defoliator 

of Douglas-fir, Pseudotsuga menziesii (Mirb.), and true firs, Abies spp., in western North 

America. Outbreaks occur quite unexpectedly every 7–10 years and usually persist for 3–4 

years. Defoliation by O. pseudotsugata can be severe and cause widespread tree mortality 

during the first year of an outbreak. Surviving trees may exhibit growth loss, top-kill, and 

tree deformity.

Sampling Procedure

Much of the information included in this review paper is summarized as individual research 

articles in Sampling Methods for Forest and Shade Tree Insects of North America, Vol. 1, 

or presented elsewhere. However, those unfamiliar with O. pseudotsugata may find this 

paper an informative source of management practices. Table 1, describing the phases of O. 

pseudotsugata densities, may be of particular interest to land managers.

Notes

Larvae of O. pseudotsugata can be sampled following the methods of Mason 1969, 1970, 

1978, 1979, and 1987; or Shepherd et al. 1985.

References

* Mason, R. R. 1969. Sequential sampling of Douglas-fir tussock moth populations. Res. 

Note PNW-102. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific 

Northwest Research Station; 11 p.

* Mason, R. R. 1970. Development of sampling methods for the Douglas-fir tussock moth, 

Hemerocampa pseudotsugata (Lepidoptera: Lymantriidae). Canadian Entomologist 102: 

836-845.
103

* Mason, R. R. 1977. Sampling low-density populations of the Douglas-fir tussock moth by 

frequency of occurrence in the lower tree crown. Res. Pap. PNW-216. Portland, OR: U.S. 

Department of Agriculture, Forest Service, Pacific Northwest Research Station; 8 p.

 

* Mason, R. R. 1978. Detecting suboutbreak populations of the Douglas-fir tussock moth by sequential sampling of early larvae in the lower tree crown. Res. Pap. PNW-238. Portland, OR: 

U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station; 9 p.

* Mason, R. R. 1987. Frequency sampling to predict densities in sparse populations of the 

Douglas-fir tussock moth. Forest Science 33: 145-156. 

* Shepherd, R. R. 1985. Pest management of Douglas-fir tussock moth: estimating larval 

density by sequential sampling. Canadian Entomologist 117: 1111-1115.

Table 1. Description of typical phases of an outbreak of the Dougls-fir tussock moth.

Phase

Density of small larvae 

(No./m2 foliage) Description

0 <3

Low density. No visible defoliation. Caterpillars uncommon, 

egg-masses rare.

I

 (Release)

30 > 3

Sub-outbreak density. Usually little or no visible defoliation. 

May be some light feeding in tops of crowns late in season. 

Caterpillars common. New egg-masses large, outnumber old 

masses.

II

 (Peak) >30

Outbreak density. Defoliation visible on most host trees, especialy severe in upper crowns. Caterpillars abundant all season. 

New egg-masses, also abundant, outnumber old masses.

III 

(Decline) >30

Outbreak density. Defoliation repeated on all trees. Caterpillars 

abundant early in season, but numbers decline sharply before 

pupation. New egg-masses scarce and smaller than usual.

IV 

(Postdecline) <3 No new defoliation. Caterpillars scarce. New egg-masses rare.

Table 1 reprinted with permission from Elsevier Limited, granted June 8, 2009.
104

Douglas Fir Tussock Moth 

Orgyia pseudotsugata (McDonnough)

Lepidoptera: Lymantriidae

Mason, R. R.; Scott, D. W.; Paul, H. G. 1993. Forecasting outbreaks of the Douglas-fir tussock moth from lower crown cocoon samples. Research Pap. PNW-RP-460. Portland, OR: 

U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station; 12 p.

Objective

To determine whether suboutbreak populations of O. pseudotsugata will develop into a damaging outbreak the following season, based on the cocoon density present in the lower crown.

Abstract

Douglas-fir tussock moth, Orgyia pseudotsugata (McDonnough), is a periodic defoliator 

of Douglas-fir, Pseudotsuga menziesii (Mirb.), and true firs, Abies spp., in western North 

America. Outbreaks occur quite unexpectedly every 7–10 years and usually persist for 3–4 

years. Defoliation by O. pseudotsugata can be severe and cause widespread tree mortality 

during the first year of an outbreak. Surviving trees may exhibit growth loss, top-kill, and 

tree deformity.

Research conducted throughout the Pacific Northwest indicates that, when O. pseudotsugata

populations are in a suboutbreak phase, the density of cocoons in the lower crown is a good 

estimator of midcrown larval density the following year. This relationship between the 

number of cocoons per 0.65 m2

 of branch area in the lower crown (X) and the number of 

small larvae per 0.65 m2

 of branch area in the midcrown (Y) is expressed by the equation Y 

= 1.70 + 55.14X (R2 = 0.66). In general, each cocoon sampled from the lower crown in the 

fall represents 55–65 larvae in the midcrown the following spring. Furthermore, the density 

of sampled cocoons is related to the level of expected defoliation the following spring. Land 

managers using this technique to forecast larval densities can decide whether the application 

of suppression tactics is necessary the following spring with sufficient time to plan operations 

over the winter months. 

Sampling Procedure

Determine the number of plots necessary to estimate cocoon densities at the desired level 

of precision, assuming 50 host trees are sampled in each plot (Table 3). Several plots of 50 

trees each should be sampled to forecast larval densities the following year. Selected stands 

should have had increasing populations of O. pseudotsugata over the past several years but 

no defoliation should be apparent.
105

Sample cocoons in the fall, after moths have emerged and laid eggs. Randomly select 50 

trees in each plot and cut three 45 cm terminal branches from the lower crown of each tree. 

Examine the underside of each branch sample for the presence or absence of O. pseudotsugata cocoons. Tally the number of 3-branch sample units with at least one cocoon present 

on a branch. Calculate the proportion of infested trees (p) using the equation p = r/n, where r 

= the total number of sampled trees with at least one cocoon present and n = the total number 

of trees sampled.

Convert the proportion of infested trees (p) into the number of cocoons per 0.65 m2

 (1000 

inch2) of branch area (X) using the equation X = -2 ln(1-p) (Mason 1977). Alternatively, 

determine the total number of cocoons per 0.65 m2 of branch area (X) using the following 

equation:

 

X =

2 yi

i = 1

n

∑

n

where yi = the number of cocoons on the ith sample unit and n = the number of sample trees.

Calculate the number of small larvae per 0.65 m2

 of branch area in the midcrown (Y) 

expected next year from the number of cocoons per 0.65 m2 of branch area in the lower 

crown (X) using the equation Y = 1.70 + 55.14X. Consult Table 4 to review the level of 

expected defoliation associated with the predicted density of young larvae the next year. The 

regression equation Y = 1.70 + 55.14X was developed from a dataset containing some very 

high densities of larvae from several geographic locations, so the model may overestimate 

subsequent larval densities more frequently than it underestimates populations.

Notes

This sampling method was developed using data from O. pseudotsugata populations that 

have been increasing in recent years but have not yet produced visible defoliation. The 

relationship between densities of cocoons and small larvae is less definable in outbreak 

conditions, when defoliation is visible on most trees, due to increasing variability produced 

by density-dependent mortality factors. 

Reference

* Mason, R. R. 1977. Sampling low density populations of the Douglas-fir tussock moth 

by frequency of occurrence in the lower crown. Res. Pap. PNW-216. Portland, OR: 

U.S. Department of Agriculture, Forest Service, Pacific Northwest Forest and Range 

Experiment Station; 8 p.
106

Table 3. Number of plots required to estimate cocoon densities at different levels of precision 

when sampling 50 trees per plota

Standard error as percent of the mean

Mean cocoon density 10 20 30 40 50 60

No./1000 in2

0.20 81 20 9 5 3 2

0.40 64 16 7 4 3 2

0.60 55 14 6 3 2 2

0.80 50 12 6 3 2 1

1.00 46 11 5 3 2 1

1.20 43 11 5 3 2 1

1.40 41 10 5 3 2 1

aSample size, n, was calculated by solving the equation n = s2

/s2x where s2 is between-plot 

variance and sx the desired standard error

Table 4. Relation of estimated cocoon densities in pre-outbreak populations of the Douglasfir tussock moth to predicted larval densities and status in the next generation.

Range of cocoon 

densities in the 

lower crown

Predicted larval 

densities in the midcrown Predicted population status

No./1000 square inches

<0.01 <2.0 Low density; no defoliation

0.01–0.30 2.0–20.0 Sub-outbreak; little or no visible defoliation

0.31–0.70 21.0–40.0 Moderate oubreak; defoliation visible on most trees

>0.70 >40.0 Severe outbreak; defoliation intense in upper crowns 

of many host trees with some trees completely 

defoliated
107

Douglas Fir Tussock Moth 

Orgyia pseudotsugata (McDonnough)

Lepidoptera: Lymantriidae

Shepherd, R. F.; Otvos, I. S. 1986. Pest management of Douglas-fir tussock moth: procedures 

for insect monitoring, problem evaluation and control actions. Research Rpt. BC-X-270. 

Victoria, B.C.: Canadian Forestry Service, Pacific Forestry Centre; 14 p.

Objective

To review the integrated pest management practices for O. pseudotsugata populations in British Columbia.

Abstract

Douglas-fir tussock moth, Orgyia pseudotsugata (McDonnough), is a periodic defoliator 

of Douglas-fir, Pseudotsuga menziesii (Mirb.), and true firs, Abies spp., in western North 

America. Outbreaks occur quite unexpectedly every 7–10 years and usually persist for 3–4 

years. Defoliation by O. pseudotsugata can be severe and cause widespread tree mortality 

during the first year of an outbreak. Surviving trees may exhibit growth loss, top-kill, and 

tree deformity.

Sampling Procedure

Much of the information included in this thorough guide has been presented as individual 

research articles in Sampling Methods for Forest and Shade Tree Insects of North America, 

Vol. 1, or the current volume. However, those unfamiliar with O. pseudotsugata may find 

this publication to be an informative source of management practices in British Columbia. 

The authors have described the identification of susceptible and infested stands; the detection 

and monitoring of O. pseudotsugata populations at low and increasing densities; the evaluation of the need for control measures; and available control options. The timeline, presented 

below, is particularly useful in summarizing the steps in the pest management process for O. 

pseudotsugata over the year.

Reference

Shepherd, R. F; Otvos, I. S.; Chorney, R. J. 1984. Pest management of Douglas-fir tussock 

moth (Lepidoptera: Lymantriidae): a sequential sampling method to determine egg mass 

density. Canadian Entomologist 116: 1041-1049.

Schedule of Procedures reproduced with the permission of the Minister of Public Works and 

Government Services, granted May 1, 2009.
108

Schedule of Procedures

The essential steps and procedures of the pest management system for the Douglas-fir tussock moth in British Columbia are listed with the approximate time of initiation relative to 

the first year of defoliation.

1. Planning stage before problems ariseIdentify stands susceptible to outbreaks by comparing overlay maps 

of historical outbreaks, forest types, plant communities and climatic 

zones.

2. July to September 

anually

Within susceptible stands, establish and maintain monitoring sites to 

determine year-to-year trends in number of moths caught in pheromone traps.

3. October annually Check pheromone traps and if catches at the permanent monitoring 

sites have increased for at least 2 years and moth density has reached 

8-10 moths/trap, an outbreak is probably only 2 years away; initiate 

step 4.

4. July of each year 

until the outbreak 

subsides

Deploy a network of auxiliary pheromone traps to help locate infested 

stands.

5. October of each 

year until the outbreak subsides

If moth counts in pheromone traps at the permanent monitoring sites 

have increased for 2–3 years and average moth density has reached 25 

moths/trap, an outbreak may be expected the following year; initiate 

step 6.

6. October of each 

year until the outbreak subsides

Search for egg masses in stands close to permanent or auxiliary traps 

that caught an average of 25 or more moths/trap. When a pocket of 

egg masses is found, determine the density through a lower-branch sequential egg-mass survey and predict the level of defoliation for next 

year (Shepherd et al. 1984); initiate step 7.

7. November of each 

year until the outbreak subsides

If egg masses are present, consider all available options for managing the insect problem. If insecticide or virus treatment or harvesting 

is chosen, conduct surveys to map infestation boundaries, determine 

areas, timber volumes, etc. as necessary and plan the operation.

8. May or June in year 

when defoliation is 

expected

If the harvest option is chosen, completely cut the infested stands and 

burn the residual slash by May prior to larval hatch. If insecticide or 

virus control is chosen, carry out application immediately after larval 

hatch and dispersal.
109

Douglas Fir Tussock Moth 

Orgyia pseudotsugata (McDonnough)

Lepidoptera: Lymantriidae

Sower, L. L.; Daterman, G. E.; Stelzer, M. J.; Nielsen, D. G. 1983. Vertical distribution of 

Douglas-fir tussock moth (Lepidoptera: Lymantriidae) eggs in suboutbreak conditions. 

Environmental Entomology 12: 1590-1591.

Objectives

To determine the vertical distribution of O. pseudotsugata egg masses; to identify growing 

populations during suboutbreak conditions using timed visual samples of foliage.

Abstract

Douglas-fir tussock moth, Orgyia pseudotsugata (McDonnough), is a periodic defoliator 

of Douglas-fir, Pseudotsuga menziesii (Mirb.), and true firs, Abies spp., in western North 

America. Outbreaks occur quite unexpectedly every 7–10 years and usually persist for 3–4 

years. Defoliation by O. pseudotsugata can be severe and cause widespread tree mortality 

during the first year of an outbreak. Surviving trees may exhibit growth loss, top-kill, and 

tree deformity.

Few studies have examined O. pseudotsugata in suboutbreak conditions, when intermediate populations begin to expand. During outbreak conditions egg masses and cocoons are 

typically found in the lower crown because high densities of larvae have defoliated the upper 

crown (Luck and Dahlsten 1967; Mason 1970). Distribution of eggs within the host tree may 

differ during suboutbreak populations, when less defoliation has occurred. 

The vertical distribution of O. pseudotsugata egg masses in suboutbreak conditions was examined on felled trees and a recommendation for timed visual samples was made for standing trees. Egg masses were found throughout the canopy but with a concentration towards 

the upper crown. Approximately 10% of the total egg masses present on the trees were visible to an observer conducting timed, 1-minute visual samples of the foliage. Thirty 1-minute observational periods of individual trees within a 20 ha area could serve as a rapid survey 

of expanding populations with outbreak potential. Damaging outbreaks during the following 

season are considered unlikely if several egg masses cannot be found within 30 minutes of 

observation. 

Sampling Procedure

Randomly select trees for sampling from a distance at which any cocoons present on the trees 
110

cannot be distinguished. At each selected tree, spend one minute visually searching for cocoons and egg masses. Focus on visually sampling the lower crown, where cocoons and egg 

masses can be seen more easily. Thirty 1-minute observational periods of individual trees 

within a 20 ha area could serve as a rapid survey of expanding populations with outbreak 

potential. Damaging outbreaks during the following season are considered unlikely if several 

egg masses cannot be found within 30 minutes of observation.

Notes

The trees selected for this study were 15–20 cm diameter at breast height, approximately 12 

m tall, and with a crown approximately 5 m wide at its widest. In addition, selected trees had 

foliage close to the ground and grew close to neighboring trees. These sampling specifications were set by the land manager and the authors’ observations may not hold for trees with 

differing characteristics. 

The authors acknowledge that a more specific sampling scheme using visual observations 

should be developed. While not explicitly stated by the authors, sampling cut trees as specified would supplement the visual sampling of the lower crown as presented here. Felled trees 

with more egg masses in the upper crown would indicate the presence of low or intermediatelevel populations, while larger numbers of egg masses in the lower crown would suggest that 

populations are building to outbreak levels, or an outbreak is underway.

References

Luck, R. F.; Dahlsten, D. L. 1967. Douglas-fir tussock moth (Hemerocampa pseudotsugata) 

egg mass distribution on white fir in northeastern California. Canadian Entomologist 

99: 1193-1203. 

* Mason, R. R. 1970. Development of sampling methods for the Douglas-fir tussock moth, 

Hemerocampa pseudotsugata (Lepidoptera: Lymantriidae). Canadian Entomologist 

102: 836-845.
111

Black Army Cutworm 

Actebia fennica (Tauscher)

Lepidoptera: Noctuidae

Shepherd, R. F.; Gray, T. G.; Maher, T. F. 1992. Management of black army cutworm. Information Rpt. BC-X-335. Victoria, B.C.: Forestry Canada; 17 p. 

Objective

To describe a monitoring system for A. fennica, with predicted defoliation levels, useful for 

managing plantings of conifer seedlings.

Abstract

Black army cutworm, Actebia fennica (Tauscher), adults are attracted to recently burned 

areas where they lay their eggs. Hatching caterpillars prefer to feed on numerous non-conifer 

food plants sprouting in burned areas, but they will increasingly feed on seedlings of numerous conifer species planted on that site as caterpillar densities increase. White spruce [Picea 

glauca (Moench) Voss] and lodgepole pine (Pinus contorta Dougl. ex Loud.) are commonly 

attacked. Older instars produce most of the damage. Defoliation reduces seedling height 

growth, may kill terminals, and inhibits root production. Feeding damage is compounded 

by moisture stress at the planting site. Sporadic but severe outbreaks of A. fennica occur in 

British Columbia. Most of the defoliation will occur the first year caterpillars appear, with 

little damage or none occurring in subsequent years. Chemical control is not advisable for A. 

fennica as severe defoliation may occur before a treatment can be applied. Management of 

A. fennica should emphasize monitoring the activity of A. fennica adults and larvae in susceptible areas and minimizing planting in areas of higher risk. 

Sites susceptible to outbreaks of A. fennica in British Columbia are found within the Engelmann spruce-subalpine fir, montane spruce, sub-boreal spruce, and interior cedar-hemlock 

biogeoclimatic zones. In particular, recently burned areas prone to droughty conditions 

should be monitored as moisture stress compounds any feeding damage produced by A. fennica. Sites with adequate moisture are less prone to extended damage and sites burned after 

September 15 are not likely to be attractive to A. fennica.

Sampling Procedure

Sampling should begin at the end of June. Use green Multi-Pher traps (model MP-1; BioContrôle Services, Ste-Foy, Quebec, Canada) baited with a rubber septum releasing 1,000 

μg of (Z)-7-dodecenyl acetate and (Z)-11-tetradecynyl acetate pheromone mixture at a ratio 

of 1:20. Traps should also hold a 2.5-cm2 square of an insecticide strip in the bottom of the 
112

trap to kill attracted moths. Place traps 0.5–1.0 m above ground and at least 100 m apart on 

south-facing slopes in areas burned by natural or prescribed fires within the past 12 months. 

One trap will service a 1-km2

 (or smaller) area if checked every 1–2 weeks to ensure it is in 

good shape and operating properly. Rodents may raid traps to feed on captured moths. Traps 

should remain in place from July 1 to September 15. Tally the total number of moths caught 

in each trap during this period and compare to the levels of caterpillar infestation and defoliation predicted for the following spring.

Moth density per trap

Predicted caterpillar 

infestation level the 

following spring

Predicted defoliation level 

of conifer seedlings the 

following spring

<350 moths Low Low risk of damage; plant in the 

spring

350–1,200 moths Moderate

Moderate risk of defoliation at 

some, but not all sites; check vegetation on site for plants favored by 

A. fennica before planting

>1,200 moths High High risk of severe defoliation

Sites identified as having a risk of a moderate caterpillar infestation level should be visited 

the following spring immediately after snow melt. Preferred food plants (Table 1) should be 

examined for feeding damage by A. fennica. Randomly select 20–30 plants at the site, including at least 5 different species. Significant damage to planted conifer seedlings is likely 

if most of the inspected host plants show “shotgun holes” or similar feeding damage. If no 

damage is observed, flag the plants, return in 1 week, and check the plants again. If damage 

is present at the second visit, and if 14 days of feeding likely remains (before the end of May 

if at low elevations), then significant damage to conifer seedlings is still possible. If no damage is found at the second visit, then significant damage to conifer seedlings is unlikely and 

planting should proceed.

For sites where a significant or high risk of defoliation is possible, consider delaying planting 

until pupation occurs (June 15 at low elevations and up to 3 weeks later at higher elevations) 

or until the following spring. Should an outbreak occur after planting, mark the area infested 

at the time of peak damage and survey the area again the following spring before replanting in that area. Replace any trees that are dead, have more than 60% defoliation, or have 

dead terminal leaders. Potential damage by A. fennica can be minimized by planting conifer 

seedlings in good growing sites with adequate soil moisture and using appropriate planting 

techniques that avoid damaging the roots. Newly planted seedlings in moist sites and with 

healthy roots, and older plantings with established root systems, may survive an unexpected 

defoliation by A. fennica. 

Notes

Damage by sphinx caterpillars (hornworms) or infection by Rhizinia root rot can be mistaken 

for damage by A. fennica. Always collect caterpillars and identify them to verify that A. fen-
113

nica is responsible for observable damage. Cutworm caterpillars typically hide in the soil 

near damaged plants during the day.

Table 1. Food plants commonly found in the Columbia Valley and Cariboo mountains listed 

from highest to lowest feeding preference by the black army cutworm.

common botanical name

Valerian Valeriana diocia

Western meadowrue Thalictrum occidentale

common horsetail Equisetum arvense

Fireweed Epilobium angustifolium

False hellbore Veratrum eschscholtzii

Heart-leaved arnica Arnica cordifolia

False Solomon’s seal Smilacina racemosa

Hooker’s fairybells Disporum hookeri

Rosy twistedstalk Streptopus roseus

Honeysuckle Lonciera spp.

Western larch Larix occidentalis

Saskatoon Amelanchier alnifolia

Rose, currants, thimbleberries Rosa, Ribes, Rubus spp.

Birch-leaved spirea Spiraea betulifolia

Bunchberry Cornus canadensis

Soopolallie Shepherdia canadensis

Aspen, willow Populus tremuloides, Salix spp.

Douglas-fir Pseudotsuga menziesii

Englemann spruce Picea engelmanii

Lodgepole pine Pinus contorta var. latifolia

Table 1 reproduced with the permission of the Minister of Public Works and Government Services, granted May 1, 2009.
114

Saddled Prominent 

Heterocampa guttivata (Walker)

Lepidoptera: Notodontidae

Spear-O’Mara, J.; Allen, D. C. 2007. Monitoring populations of saddled prominent (Lepidoptera: Notodontidae) with pheromone-baited traps. Journal of Economic Entomology 

100: 335-342.

Objective

To monitor H. guttivitta populations in sugarbush using pheromone traps; to predict mean 

peak flight and flight termination by H. guttivitta using a degree-day model.

Abstract

Saddled prominent, Heterocampa guttivitta (Walker), is a periodic defoliator of sugar maple 

(Acer saccharum Marsh.), and other hardwoods in northern USA. Populations of H. guttivitta are of a concern in sugarbush management as multiple years of heavy defoliation results in 

extensive crown damage with reduced yield and quality of sap in healthy, growing trees and 

mortality in younger, understory maples. Trends in H. guttivitta populations can be estimated using bucket traps baited with a 90/10 blend of Z13-hexdadecen-11-yn-1-yl aldehyde 

(Z13Y11-16:ALD) and hexadec-11-yn-1-yl aldehyde (Y11-16:Ald) with sufficient time for 

sugarbush operators to consider management options for this pest. Egg density approximates 

1.0 viable eggs per 10 leaf clusters when the average peak trap catch equals 70-80 moths. At 

this approximate density, defoliation of terminal branches in overstory maples becomes visible from the ground level and warrants control measures. Using a base temperature of 5.5°C 

with temperature data provided by the closest weather stations, mean peak flight and termination of flight period occurred at 304 ± 10 and 571 ± 8 degree-days, respectively.

Sampling Procedure

Traps should be installed by late May. Use either Pherocon 1C wing sticky traps (Trécé 

Inc., Salinas, CA) or green Multipher bucket traps (Le Groupe Biocontrol, Ste. Foy, Quebec). High-capacity, nonsaturating bucket traps are preferable when moth populations are 

high because sticky traps will saturate quickly. However, sticky traps may be more effective 

than bucket traps when H. guttivitta populations are very low. Bait traps of either type with 

rubber septa containing 10 μg of a 90/10 blend of Z13Y11-16:ALD and Y11-16:Ald (Research and Productivity Council, Fredericton, New Brunswick). Install a 90 x 25 mm strip 

of insecticide-impregnated tape in each trap to kill attracted moths. Hang traps below the 

canopy of each stand and approximately 2 m above the ground. Use a stake with a cross bar 

instead of hanging traps directly on a tree branch. Place traps at least 20 m within the stand, 
115

with 40 m between traps. Service traps every 4 days and leave in place until termination of 

the flight period. Mean peak flight and termination of flight period can be predicted at 304 ± 

10 and 571 ± 8 degree-days, respectively, using a base temperature of 5.5°C with temperature 

data provided by the closest weather stations. Sugarbush operators should consider management options when the average peak trap catch equals 70-80 moths, which approximates the 

threshold of defoliation on branch terminals that is visible from the ground.

Notes

Sugarbush management represents the management of sugar maple for its sap to make maple 

syrup or sugar. Conspecific notodontid species were not attracted to the pheromone blend. 

The authors did not suggest a specific number of traps to be installed in each stand, but 5 

sticky traps were placed in each stand in this study. Estimates of H. guttivitta populations 

should consider the density of sugar maple to other favored hardwoods, particularly American beech (Fagus grandifolia Ehrh.), within the stand.
116

Variable Oakleaf Caterpillar

Heterocampa manteo (Doubleday)

Lepidoptera: Notodontidae

Surgeoner, G. A.; Wallner, W. E. 1978. Foliage consumption by the variable oak leaf caterpillar, Heterocampa manteo (Lepidoptera: Notodontidae), its use in defoliation predictions. 

Canadian Entomologist 110: 241-244.

Objective

To predict defoliation by H. manteo on Quercus spp. based on the density of early instars.

Abstract

Variable oakleaf caterpillar, Heterocampa manteo (Doubleday), commonly defoliates oaks, 

Quercus spp., and other hardwoods in eastern North America. White oak, Quercus alba L., 

is a preferred host of H. manteo. Periodic outbreaks reduce tree growth but typically subside 

after 2-–3 years and before tree mortality occurs. Two generations occur in the south and one 

generation occurs in the north.

Foliage consumption by H. manteo on leaves of both white and red, Quercus rubra L., oaks 

was determined in the lab. An average of 6.9 and 5.2 leaves per larva were observed for 

white and red oak leaves, respectively. Life studies of H. manteo conducted in Michigan indicated predation had little impact on larval survival, therefore a crude prediction of expected 

defoliation can be made from the density of early instars on oaks. Complete defoliation 

is likely when early instars average one per 5 leaves, and 50% defoliation can be expected 

when densities average one per 10 leaves.

Sampling Procedure

Randomly sample terminals from canopies of oak trees, using a pole pruner if necessary. Examine the leaves of each terminal for the presence of early instar H. manteo. First instars are 

gregarious on the underside of leaves. Expect 50 and 100% defoliation when early instars 

average one per ten and five leaves, respectively.

Notes

The authors did not specify how many terminals per tree, trees per stand, or leaves per terminal 

should be sampled to predict defoliation by H. manteo. Land managers should examine a sufficient number of trees and leaves per tree to adequately sample the pest population throughout 

the area of concern. Defoliation estimates will vary if larval parasitism is prevalent as parasitized larvae consume significantly less foliage. 
117

Pine Butterfly

Neophasia menapia (Felder & Felder)

Lepidoptera: Pieridae

Cole, W. E. 1956. Surveys and control methods of the pine butterfly during an outbreak in 

southern Idaho, 1953-1954. Res. Note RN-30. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station; 9 p.

Objective

To develop appraisal surveys for N. menapia for evaluation of spray treatment efficacy or 

predict infestations requiring control measures.

Abstract

Pine butterfly, Neophasia menapia (Felder & Felder), has been a serious pest of ponderosa 

pine, Pinus ponderosa P. & C. Lawson, and other Pinus spp. in the northwest USA. The caterpillars consume all age classes of pine needles, causing significant defoliation and mortality 

of infested trees. Aerial spray operations were initiated against this pest, but managers had 

no feasible sampling method to evaluate treatment efficacy. A sampling scheme is described 

for the eggs of N. menapia, the easiest life stage to sample this insect.

Sampling ridges was preferred over creek bottoms due to reduced variance in estimates from 

ridges. Greater than 10, 5–9, 2–4 and <2 eggs per 38 cm twig sample of new growth indicated epidemic, heavy, light, and endemic population of pine butterfly, respectively. Controls 

were warranted at densities of >5 eggs per twig.

Sampling Procedure

Sampling intensity and distribution of samples depend on the density and distribution of the 

butterfly population in the area of concern. Avoid sampling in creek bottoms as the distribution of pine butterfly is patchy and therefore requires significantly greater sampling effort to 

estimate populations within 25% of the mean. 

To evaluate treatment efficacy, randomly select a survey line parallel to aerial spray lines. 

Select a tree every 100.6 m (5 chains) until the required number of trees have been sampled. 

If the spray block is too short to accommodate all trees on one line, start another line in the 

same spray block or area of concern. Randomly sample the required number of twigs from 

ground level to a height of ≈7 m above ground. The following recommendations, based on 

a 68% confidence interval with a 25% standard error of the mean, generally gave acceptable 

precision for management purposes:
118

1. Heavy infestations: sample two 38 cm twigs from each of 10 sequentially selected 

trees per area, or 10 twigs from each of three trees.

2. Moderate infestations: sample five 38 cm twigs from each of 20 trees per area.

3. Light infestations: sample five 38 cm twigs from each of 20 trees per area.

4. Endemic infestations: sample five 38 cm twigs from each of 25 trees per area.

To predict infestations requiring control measures, sample trees as described above. Population estimates are further refined as:

1. Epidemic infestation: ≥10 eggs per 38 cm twig

2. Heavy infestation: 5–9 eggs per 38 cm twig

3. Light infestation: 2–4 eggs per 38 cm twig

4. Endemic infestation: <2 eggs per 38 cm twig

Control measures are warranted if the average density of pine butterfly eggs on new growth 

is >5 per twig.

Note

This sampling scheme was developed in Idaho and should be used with caution until validated in other areas.
119

Bagworm

Thyridopteryx ephemeraeformis (Haworth)

Lepidoptera: Psychidae

Raupp, M. J.; Davidson, J. A.; Koehler, C. S.; Sadof, C. S.; Reichelderfer, K. 1988. Decisionmaking considerations for aesthetic damage caused by pests. Bulletin of the Entomological 

Society of America 34: 27-32.

Objective

To provide a sampling plan for T. ephemeraeformis with reference to quantified economic 

and aesthetic injury levels for making management decisions.

Abstract

Bagworm, Thyridopteryx ephemeraeformis (Haworth), is a defoliator of many trees and 

shrubs, including arborvitae, Thuja occidentalis L.. Bagworm larvae are covered by a 

characteristic loose silk bag covered with bits of organic material, which they construct for 

concealment. Larvae hang on the branches and feed on the foliage of the host tree, and high 

densities of bagworms give the tree the appearance of being covered with unsightly brown 

cones. 

An economic injury level (EIL) was developed for retail nurserymen based on the estimated 

cost of management, the market value, and the injury produced by T. ephemeraeformis for a 

1.2-m arborvitae. The EIL was estimated to be 4 first instar T. ephemeraeformis per 1.2-m 

tree. The low value of the EIL reflects the low tolerance of homeowners and their perceptions of greatly reduced value associated with the presence of the insect on an ornamental 

plant. 

An aesthetic injury level (AIL) was developed for bagworm based on when half the customers at a nursery retail center would initiate control measures for the pest. The relationship 

between when customers believed the arborvitae had been damaged by T. ephemeraeformis 

and when control measures were required was quantified. Regression analysis indicated that 

half of the polled customers perceived the foliage injury produced by approximately 9 first 

instars per 1.2-m of tree height sufficient to warrant control measures. This aesthetic threshold corresponds to approximately 7% of the crown being discolored or defoliated. Half of 

the polled customers would consider the foliage injury produced by approximately 6 first 

instars per 1.2-m tree as damaging to the plant. This AIL represents <8% of the crown being 

discolored or defoliated. 
120

Sampling Procedure

Examine arborvitae for the presence of T. ephemeraeformis in its characteristic bags. If the 

density of T. ephemeraeformis on an individual tree is ≥6 first instars per 1.2 m of tree stem, 

nursery managers should initiate control measures to maintain a desirably high aesthetic 

quality of ornamental arborvitae. 

Notes

The AIL of 6 or more first instar T. ephemeraeformis per 1.2 m of arborvitae stem, based on 

customer perception of aesthetic damage, is marginally higher than the EIL of 4 first instars 

per 1.2 m of tree stem, which was based on economic parameters. The actual injury by T. 

ephemeraeformis observed on arborvitae during this study was largely cosmetic as the plants 

tolerated high levels of defoliation without associated mortality and injury was not evident 

several years later after refoliation.
121

Orangestriped Oakworm

Anisota senatoria (J.E. Smith)

Lepidoptera: Saturniidae

Coffelt, M. A.; Schultz, P. B. 1993a. Relationship among orangestriped oakworm 

(Lepidoptera: Saturniidae) frass length, frass production, host plant, and defoliation. 

Journal of Entomological Science 28: 291-298.

Objective

To relate A. senatoria frass length and quantity to defoliation levels on pin oak.

Abstract

The orangestriped oakworm, Anisota senatoria (J.E. Smith), is a native defoliator of various 

oaks, Quercus spp., and other hardwood species in the eastern US and Canada. Severe outbreaks of A. senatoria occurred in some urban areas of Virginia in the early 1990s, leading to 

the development of integrated pest management strategies. Coffelt and Schultz (1990) developed an aesthetic injury level of 25% defoliation for A. senatoria on urban pin oak, Quercus 

palustris Muenchhausen. In the urban landscape, citizens objected to frass accumulation and 

noticeable larvae more than actual defoliation by A. senatoria (Coffelt and Schultz 1990). 

Field and laboratory experiments were designed to develop decision-making guidelines for A. 

senatoria management based on frass measurements. Frass length and production per larva 

were affected by Quercus spp. host, but mean frass length could be used to estimate which 

instars were present on pin oak. The presence of early instar A. senatoria may signal the 

need to consider control measures in the urban landscape. Frass production on pin oak was 

positively related to percentage defoliation (y = 24.12 + 0.43x; r2

 = 0.62), allowing for the 

prediction of defoliation levels based on frass accumulation. Using the aesthetic injury level 

developed by Coffelt and Schultz (1990), 2.2 g of frass collected on a 2.62 m2

 circle of fabric 

under a tree equated to 25% defoliation on pin oak and would thus warrant control measures.

Sampling Procedure

Place a circular piece (2.62 m2) of polypropylene landscape fabric under the canopy of selected pin oaks in late July. Leave fabric circles in place until late August, then weigh all the 

frass pellets collected on the fabric to the nearest gram. Use the equation y = 24.12 + 0.43x, 

where y = percent defoliation and x = frass weight in g per 2.62 m2, to predict defoliation. 

Frass production of 2.2 g per 2.62 m2

 circle of fabric would warrant control measures if using 

the aesthetic injury level of 25% defoliation.

To determine if early instars are present, take a subsample of 25 frass pellets from each tree. 
122

Measure pellet length to the nearest 0.01 mm using a stereomicroscope fitted with an ocular 

micrometer. Compare the average length of collected pellets to those presented in Table 1 to 

determine the stage of instars present on the tree. The presence of early instars could indicate 

the need for control strategies.

Notes

This technique assumes that the operator has a certain familiarity with the characteristics of 

lepidopteran frass. The presence of A. senatoria larvae should be verified on sampled trees 

before assuming any collected frass was produced by A. senatoria. The relationship between 

frass production and percent defoliation on pin oak was developed from data taken on small 

trees approximately 2 m tall and with a 6.3 cm diameter breast height. Further research is 

needed to determine if the relationship is valid for larger pin oaks, or for oaks of different 

species.

Reference

Coffelt, M. A.; Schultz, P. B. 1990. Development of an aesthetic injury level to decrease 

pesticide use against orangestriped oakworm (Lepidoptera: Saturniidae) in an urban pest 

management project. Journal of Economic Entomology 83: 2044-2049.

Table 1. Mean frass length and total frass produced by A. senatoria instars, 1989 field experiments.*

Instar Frass Length (mm) n Total Frass Produced† n

First 0.58 ± 0.003 e 15 4.96 ± 0.33 c 15

Second 1.06 ± 0.01 d 9 12.63 ± 1.76 c 15

Third 1.64 ± 0.02 e 11 35.54 ±3.89 c 15

Fourth 2.57 ± 0.05 b 11 233.65 ± 17.5 b 11

Fifth 3.33 ± 0.09 a 6 340.33 ± 70.8 a 6

* Means (± SEM) within columns followed by the same letter are not significantly different 

(P > 0.05) (Student-Newman-Keuls test) (SAS Institute 1985). 

†Total frass produced by instar of an A. senatoria group with an estimated initial mean population size of 485 larvae.

Table 1 reproduced with the permission of the Journal of Entomological Science, granted April 2, 2009.
123

Orangestriped Oakworm

Anisota senatoria (J.E. Smith)

Lepidoptera: Saturniidae

Coffelt, M. A.; Schultz, P. B. 1993b. Quantification of an aesthetic injury level and threshold for an urban pest management program against orangestriped oakworm (Lepidoptera: 

Saturniidae). Journal of Economic Entomology 86: 1512-1515.

Objective

To develop thresholds for densities of A. senatoria egg masses on trees of increasing size that 

are predictive of the 25% leaf defoliation level on pin oak, Quercus palustris Muenchhausen.

Abstract

The orangestriped oakworm, Anisota senatoria (J.E. Smith), is a native defoliator of various 

oaks, Quercus spp., and other hardwood species in the eastern U.S. and Canada. Outbreaks 

became severe in some urban areas of Virginia in the early 1990s, leading to the development 

of integrated pest management strategies. Coffelt and Schultz (1990) developed an aesthetic 

injury level of 25% defoliation for A. senatoria based on estimating defoliation levels each 

day. This procedure was time consuming and often control measures were applied too late to 

protect trees from significant defoliation.

The authors developed an alternative method of predicting defoliation levels on urban pin 

oak, Quercus palustris Muenchhausen, by relating egg mass density to subsequent defoliation by A. senatoria. The density of egg masses was related positively to defoliation level 

on pin oaks for four size classes (12.6, 19.1, 26.4 and 35.4 cm DBH). Applying the 25% 

defoliation threshold to size class gave egg mass density thresholds of 0.9, 4.8, 6.6, and 9.0 

per tree. Sampling egg masses should provide managers sufficient time to make control 

decisions before the aesthetic injury level is exceeded. Because A. senatoria larvae remain 

in gregarious clusters as young instars, these thresholds are also applicable to first and second 

instars.

Sampling Procedure

Measure the DBH of each pin oak sampled and assign the tree to the nearest DBH class 

(12.6, 19.1, 26.4 or 35.4 cm). Although not explicitly stated in the article, rounding DBH to 

the nearest cm is probably sufficient.

Orangestriped oakworm lays its eggs on the underside of oak leaves on terminal shoots in 

July in eastern Virginia. The yellow egg masses are easily spotted, but they may be more vis-
124

ible when sampling on a cloudy day with less glare. Walk completely around a selected pin 

oak and count the number of egg masses visible on the underside of the leaves in the lower 

canopy (P. Shultz, pers. comm.). Compare the estimate with the threshold value of egg mass 

density that may result in 25% or more defoliation (see Table 1). Control measures should 

be applied if the aesthetic injury level of 25% defoliation will be exceeded on trees with high 

aesthetic value.

Note

This sampling plan should only be used for A. senatoria infesting pin oak as the thresholds 

may vary with other species of oaks. 

 References

* Coffelt, M. A.; Schultz, P. B. 1990. Development of an aesthetic injury level to decrease 

pesticide use against orangestriped oakworm (Lepidoptera:Saturniidae) in an urban pest 

management project. Journal of Economic Entomology 83: 2044-2049.

Table 1. Mean diameter and height of Q. palustris and number of A. senatoria egg masses 

that caused 25 and 100% defoliation, 1987–1990.

Tree DBHa

(rounded to nearest cm)

DBH 

Range

No. egg masses that 

cause 25% defoliation

No. egg masses that 

cause 100% defoliation

12.6 8.9–13.7 0.9 4.0

19.1 14.0–22.6 4.8 14.5

26.4 23.0–30.0 6.6 17.0

35.4 31.0–40.4 9.0 28.3

a

DBH, diameter at breast height.

Table 2 modified and reproduced with permission from the Journal of Economic 

Entomology, granted April 2, 2009.
125

Western Blackheaded Budworm

Acleris gloverana (Walsingham)

Lepidoptera: Tortricidae

Gray, T. G.; Shepherd, R. F.; Wood, C. S. 1973. A hot-water technique to remove insect eggs 

from foliage. Canadian Forest Service Bi-monthly Res. Notes 29: 29.

Objective

To develop a hot water extraction technique useful for separating A. gloverana eggs from 

hemlock foliage.

Abstract

Western blackheaded budworm, Acleris gloverana (Walsingham), is an occasional pest of 

western hemlock, Tsuga heterophylla (Raf.) Sarg., in the western US and Canada. An extraction technique using hot water was developed to remove A. gloverana eggs from hemlock 

branches in less time and with greater reliability than a sodium hydroxide solution soak. Using this technique, two people can process up to 200 branches a day if screens are not used. 

Two people can process 70 branches a day if samples require the use of screens to remove 

debris before filtration. 

Sampling Procedure

Randomly sample 46-cm branch tips from trees in the area of concern. Soak each branch individually in a 3,000 ml beaker of boiling water (100 oC) for a maximum of 30 secs. Soaking 

the branches for longer will remove all the foliage in addition to the eggs, requiring additional screening of the needles from the sample. Using tongs, agitate the branch in the water 

to ensure that all A. gloverana eggs are released from the foliage. Eggs will settle to the 

bottom of the beaker. Remove the branch and slowly pour the water containing the eggs into 

an 18.5-cm Buchner funnel. Rinse the beaker with additional water to ensure no eggs remain 

and pour this rinsate into the funnel as well. Vacuum filtrate the material through filter paper. 

A plexiglass ring can be used to weight the filter paper down and ensure that eggs stay on the 

upper surface of the paper. This ring should also be rinsed adequately and the rinsate filtered 

as well. 

Cumbersome branches should be clipped into smaller segments to fit in the beaker, but this 

will increase the amount of debris in the water. Debris can be removed by pouring the contents of the beaker onto a #20 mesh screen fitted onto a #50 mesh screen (US Series Equivalent). The top screen will filter coarse debris while the bottom screen will capture the eggs. 

Wash the debris on the top screen with additional rinses using plenty of water. When done, 
126

wash the bottom screen into a funnel fitted into a 1-liter container. Use plenty of water to 

remove all the eggs on the screen, then vacuum filtrate as described above. 

Notes

Express egg density as the total number of eggs counted on the filter paper divided by the initial wet weight of the 46-cm branch sample. Filter papers may be refrigerated or frozen for 

counting at a later date; store labeled filter papers between polyethylene sheets. The authors 

showed that a single wash using this technique recovered 84% of the total eggs while three 

washes recovered 98% of the total. This method may be useful with other lepidopteran species, but should be tested and verified before use.
127

Western Blackheaded Budworm

Acleris gloverana (Walsingham)

Lepidoptera: Tortricidae

Harris, J. W. E.; Collis, D. G.; Magar, K. M. 1972. Evaluation of the tree-beating method for 

sampling defoliating forest insects. Canadian Entomologist 104: 723-729.

Objective

To reduce sampling effort and improve sampling accuracy for populations of A. gloverana.

Abstract

Western blackheaded budworm, Acleris gloverana (Walsingham), is an occasional pest of 

western hemlock, Tsuga heterophylla (Raf.) Sarg., in the western US and Canada. The Canadian Forestry Service includes this species in its annual Forest Insect and Disease Survey 

(FIDS). The authors focused on A. gloverana in a study conducted to examine the appropriateness of the standard 3-tree sample used to monitor populations of insect defoliators. The 

standard method of sampling 3 trees using a beat sheet was appropriate for A. gloverana as 

large variances were associated with samples of only 1 or 2 trees. A total of 17 samples, 

calculated from 51 trees, were required for estimating low populations of A. gloverana over 

a sampling area of 60,703 hectares, with a 95% confidence level of remaining within 50% 

of the arithmetic mean. This relatively small sampling effort was deemed appropriate as the 

survey focuses on broad population changes over large areas. In addition, samples collected 

from roadsides did not vary significantly from those collected randomly throughout the area 

or interest, or those collected from stands of a certain percentage of tree species. Weather did 

affect the abundance of A. gloverana, with fewer larvae collected in the rain or on wet foliage.

Sampling Procedure

Randomly sample 3 trees of each species of interest at each selected site by laying a 2.1 by 

2.7 m white sheet under a tree and beating the branches with a 3.7 m pole for approximately 

30 secs. Identify and count the A. gloverana larvae that fall onto the sheet. Sampling from 

locations that are easily accessible by roads or waterways, i.e. “roadside sampling”, is convenient, economical, and statistically appropriate. To obtain a true estimate of larval populations, surveys for A. gloverana should be conducted in dry weather when the foliage is not 

wet. Current infestation levels can be compared to previous FIDS reports as a rough interpretation of whether populations are increasing, decreasing, or remaining stable. 
128

Notes

The green-striped forest looper, Melanolophia imitata (Walker), is a common defoliator of 

conifers that produced top-kill and tree mortality in western hemlock in British Columbia 

during the 1960’s, but has not had an outbreak since then. The authors recorded data on M. 

imitata during this study and found similar results as for A. gloverana, with the exception that 

M. imitata was less abundant on clear, sunny days. A total of 16 samples, calculated from 

51 trees, was required for estimating low populations of M. imitata over a sampling area 

of 60,703 hectares, with a 95% confidence level of remaining within 50% of the arithmetic 

mean.
129

Western Blackheaded Budworm

Acleris gloverana (Walsingham)

Lepidoptera: Tortricidae

Shepherd, R. F.; Gray, T. G. 1990. A sampling system for eggs of western blackheaded budworm, Acleris gloverana (Walsingham) (Lepidoptera: Tortricidae), on western hemlock, 

Tsuga heterophylla (Raf.) Sarg. Canadian Entomologist 122: 555-562.

Objective

To develop a sampling plan that estimates the density of A. gloverana at three levels of fixed 

precision and relates density to predicted damage levels.

Abstract

Western blackheaded budworm, Acleris gloverana (Walsingham), is an occasional pest of 

western hemlock, Tsuga heterophylla (Raf.) Sarg., in the western US and Canada. A sequential estimation plan was developed to improve the efficiency of sampling A. gloverana eggs. 

Previous work described the relationship between egg density and defoliation levels of A. 

gloverana (Silver 1959), but it was labor intensive. The method described here would allow 

managers to efficiently estimate egg densities to predict defoliation levels of A. gloverana.

The fresh weight (or volume) of a 46-cm branch tip per tree is used as the sample unit for the 

estimation of density of eggs of A. gloverana. Branch weight is less variable among crown 

levels and cardinal points on the tree, and so fewer sample units are required than if using 

branch volume. The average time to select a tree, remove a sample, and process the sample 

is 21 minutes. 

Sampling Procedure

Randomly sample one 46-cm branch tip per tree in the area of concern. Soak each branch 

individually in a 3,000 ml beaker of hot water for a maximum of 30 secs (Gray et al. 1973; 

see our summary in this volume). Soaking the branches for longer will remove all the foliage 

in addition to the eggs, requiring additional screening of the needles from the sample. Using 

tongs, agitate the branch sample in the water to ensure that all A. gloverana eggs are released 

from the foliage. Eggs will settle to the bottom of the beaker. Remove the sample branch 

and slowly pour the water containing the eggs into an 18.5-cm Buchner funnel. Rinse the 

beaker with additional water to ensure no eggs remain and pour this rinsate into the funnel 

as well. Vacuum filtrate the material through filter paper. A plexiglass ring can be used to 

weight the filter paper down and ensure that eggs stay on the upper surface of the paper. This 

ring should also be rinsed adequately and the rinsate filtered as well. 
130

Count the number of A. gloverana eggs on the filter paper and divide the count by the fresh 

weight of the sampled branch. Sample a minimum of four trees as described above. Keep 

a cumulative tally of egg densities, rounding to the nearest egg per gram of fresh branch 

weight. Refer to Table 2 for the desired level of fixed precision and stop sampling once the 

cumulative egg density is equal to or exceeds the appropriate threshold. Compare the estimated density with Table 3 to determine the predicted defoliation class.

Notes

This method should only be used before serious defoliation has occurred in the tree. High 

levels of defoliation alter the distribution of needles, which are oviposition sites for this insect, within the tree crown. This changes the relationship between mean crowding and insect 

density and changes the relationship between required sample size and egg density at each 

level of fixed precision used.

Filter papers may be refrigerated or frozen for counting at a later date; store labeled filter 

papers between polyethylene sheets.

References

Gray, T. G.; Shepherd, R. F.; Wood, C. S. 1973. A hot-water technique to remove insect eggs 

from foliage. Canadian Forest Service Bi-monthly Res. Notes 29: 29.

Silver, G. T. 1959. A method for sampling eggs of the blackheaded budworm. Journal of 

Forestry 57: 203-205.
131

Table 2. The cumulative egg densities per gram of fresh branch weight for different required 

sample sizes to attain allowable errors of 0.10 and 0.20. Sample densities are determined for 

each branch and accumulated. Stop sampling when the accumulated sample data equals or 

exceeds the egg density of the chosen allowable error.

Number of 

branches 

sampled

Enter 

accumulated 

sample data

Allowable error

0.20% 0.10%

Accumulated egg densities per gram of fresh weight

4 14 -

5 9 -

6 7 -

7 6 -

8 6 -

9 5 -

10 5 -

11 5 -

12 5 -

13 5 175

14 4 100

15 4 70

16 4 56

20 4 35

30 4 23

40 4 20

50 4 18

Table 3. Range of egg densities per branch and per gram of fresh branch weight for three 

defoliation classes.

Damage class Number of eggs per 46cm branch

Number of eggs per gram 

of fresh weight

Nil to light 0–26 0–1.40

Moderate 27–59 1.41–3.20

Severe 60 + 3.21

Table 2 and 3 reprinted as fair us under 17 USC § 107.
132

Two-year-cycle Spruce Budworm

Choristoneura biennis Freeman

Lepidoptera: Tortricidae

Nealis, V. G.; Turnquist, R. 2003. Predicting defoliation by Choristoneura biennis (Lepidoptera: Tortricidae). Canadian Entomologist 135: 903-907.

Objective

To predict defoliation within stands by C. biennis in its second year based on feeding damage 

observed during its first year.

Abstract

Two-year-cycle spruce budworm, Choristoneura biennis Freeman, occurs exclusively in high 

elevation stands of subalpine fir, Abies lasiocarpa Hook., and white spruce, Picea glauca

(Moench) Voss, in British Columbia. During the first year, the insect develops to the fourth 

instar and overwinters. In the second year, the fourth instars resume feeding, pupate, emerge 

as adults, and lay eggs. Their progeny develop until the second instar and then overwinter, 

completing the 2-year life cycle. The last three instars of C. biennis (i.e., fourth, fifth, and 

sixth instars) produce most of the defoliation of subalpine fir and white spruce. Periodic outbreaks occur every 30 years and can last 5–10 years.

Most surveys for C. biennis rely on direct sampling of egg masses or larvae, but egg mass 

density is a weak predictor of larval density or defoliation. Historical and contemporary survey data were used to develop a method of estimating the defoliation within stands expected 

the second year of the C. biennis life cycle, based on feeding damage observed during the 

first year. A strong linear relationship exists between the percentage of shoots damaged by 

first-year larvae and the percentage of defoliation produced by larvae in their second year (r2

= 0.78; P < 0.001). This relationship is expressed by the equation d = -4.24 + 1.053x, where 

d = the percentage of defoliation expected in the second year, x = the percentage of shoots 

damaged in the first year of feeding, and both d and x are arcsine-transformed values. 

Feeding damage is relatively easy to assess using the method of Fettes (1950) and is not subject to the sampling bias of observers who may fail to find all C. biennis larvae in a branch 

sample. Land managers can use the predicted defoliation levels to judge whether control 

measures are necessary in the upcoming year.

Sampling Procedure

Establish 50 x 50 m plots at least 50 m inside the forest boundary. Each corner of the plots 
133

should have clusters of codominant white spruce and subalpine fir. Sample plots only after 

first-year larvae have finished feeding. Randomly select 10 mature trees of both white spruce 

and subalpine fir within the four corner clusters (10 trees per species/plot). Randomly cut 

one or two 45-cm branch tips from each tree using pole pruners. Bag and return branches to 

the laboratory. Visually estimate the percentage of defoliation (needles removed) on currentyear shoots of each branch using the illustration in Figure 6 from Fettes (1950). 

Convert the defoliation class values to percentage shoots damaged using the mid-point of the 

defoliation class as the mean. Calculate the percentage of shoots damaged within the stand 

by pooling all branch data for both white spruce and subalpine fir. 

Use the following equation to estimate defoliation in the subsequent year:

d = –4.24 + 1.053x

where d = the percentage of defoliation expected in the second year, x = the percentage of 

shoots damaged in the first year of feeding, and both d and x are arcsine-transformed values. 

Reference

Fettes, J. J. 1950. Investigations of sampling techniques for population studies of the spruce 

budworm on balsam fir in Ontario. Annual Rep. 1949. Sault Ste. Marie: Canadian Forest 

Service, Forest Insect Lab; 11 p.
134

Fig. 6. Fettes method of estimating defoliation.

Figure 6 taken from: 

Montgomery, B. A.; Simmons, G. A.; Witter, J. A.; Flexner, J. L. 1982. The spruce budworm handbook: a management guide for spruce-fir stands in the Lake States. Handb. 

82-7. Ann Arbor, MI: Michigan Cooperative Forest Pest Management Program; 35 p.
135

Spruce Budworm

Choristoneura fumiferana (Clemens)

Lepidoptera: Tortricidae

Fowler, G. W.; Simmons, G. A. 1985. Spruce budworms handbook: sampling procedures for 

spruce budworm egg-mass surveys (with reference to the Lake States). Agric. Handb. 635. 

Washington, D.C.: U.S. Department of Agriculture, Forest Service; 33 p. 

Objectives

To summarize the methods used to estimate the density of C. fumiferana egg masses in 

spruce-fir stands and calculate the associated variance of the mean, the allowable error percentage, and the 75% upper and lower error boundaries.

Abstract

Spruce budworm, Choristoneura fumiferana (Clemens), is the most destructive defoliator 

of balsam fir, Abies balsamea (L.) Mill., and white spruce, Picea glauca (Moench) Voss, in 

eastern North America. The last three larval instars cause most of the defoliation. Periodic 

outbreaks occur every 30 years, while epidemics can last 5–10 years.

The methods for sampling C. fumiferana egg masses summarized in this handbook are a 

compilation resulting from a cooperative effort between the U.S. Department of Agriculture 

and the Canadian Department of the Environment (CANUSA). Sampling procedures and associated statistics are described in detail for single stands as well as forest management units 

and larger areas. A system to determine the hazard rating and treatment priority of stands 

is presented as well. These methods are appropriate for pure or mixed stands of white and 

black spruce, Picea mariana (P. Mill.) B.S.P., or balsam fir.

Sampling Procedure

Estimating egg mass density for a single stand: Choose high risk stands to sample based 

on whether an outbreak is in progress, the stand value is high, the stand is ≥2 ha, the stand 

contains more than 30% spruce-fir, dominant/codominant trees are ≥9 m tall, and the area is 

accessible for management and harvesting. Determine the number of plots appropriate for 

the size of the stand based on the following:
136

Stand size Size and rank of stand

Recommended number of 

sampling plots (consisting of 3 

trees each)

<10 ha Small, rank 1 2

10–20 ha Medium, rank 2 3

20–40 ha Large, rank 3 4

>40 ha Very large, rank 4 5

Determine where to place the plots using a compartment map or aerial photo of the stand. 

Draw a line bisecting the long axis of the stand. Divide the number of samples to be taken 

into the length of the line to determine the interval between sample points on the line. Use a 

random number generator to pick a random number between 0 and the interval length. This 

number determines how far into the plot the first sample point will be located. Space the 

remaining sample points along the line from this initial point using the interval length.

In the field, randomly select the 3 host trees closest to the designated sample point. Selected trees should be either spruce or fir if the stand is mixed. Trees should be dominant/

codominant and between 9–18 m tall, have a 50–100% live crown ratio, and be relatively 

open grown. For each tree in a sampling point, cut two branches randomly selected from the 

midcrown of each sample tree, being sure that each sample includes all the live foliage on the 

branch. Clip all the live foliage off the branches and place together in one large bag labeled 

with the date, stand number or name, the plot number, and the tree number.

In the laboratory, carefully measure the length and width of foliage on each branch and 

record the measurements on the data sheet (Table 1). Clip the foliage into 10–15 cm sections and examine each for fresh egg masses. Any egg masses on new foliage are considered 

fresh. Egg masses on old foliage are considered fresh if they are a clean white, fully inflated, 

and show no signs of damage or parasitization. Grey, deflated, or otherwise damaged egg 

masses are considered old. Egg masses with at least 50% black eggs are considered parasitized. Remove all egg masses and needles and place in a container. Record the total number 

of fresh egg masses and the total number of parasitized egg masses found on the datasheet. 

Return the branch segments and egg masses back to the labeled bag if a checker is re-examining bags for searcher accuracy.

To check the accuracy of personnel searching for egg masses, re-examine 25–50 randomly 

selected bags previously processed by one searcher. The original searcher should not know 

which bags will be selected and should not do the re-examination. Using Table 2, calculate 

the accuracy proportion of each searcher by adding the total number of new egg masses 

found in a bag by the first searcher to the number of new egg masses found during re-examination. Divide this number into the total number of new egg masses found in a bag by the 

first searcher to generate the accuracy proportion for the searcher. Correct future egg mass 

counts by that searcher by dividing the number of egg masses found in a bag by the accuracy 

proportion calculated for that searcher.
137

Accuracy Total number new egg masses found by searcher

proportion = Total number new egg masses found by searcher + total number of egg 

masses found during re-examination

As a rule, all samples should be checked twice for accuracy if populations of spruce budworms are low (>0.4 egg mass/1,000 cm2

 foliage). There is no need to calculate a correction 

factor in this case.

Use the following formulae to calculate the average stand egg mass density (SA), the variance of the average [s2

(SA)], the allowable error percent (AE%), and the 75% error boundaries per stand:

 

SA =∑PDi / n

where n = the number of plots of three trees each, PDi

 = ∑ Tj/3 represents the plot average 

egg mass density for the ith plot, and Tj is the tree egg mass density for the jth tree as 

determined from the two branches sampled from that tree.

 

s

2

( ) SA = PDi

2

∑ − ( ) ∑PDi

2

/ n

n n( ) −1

AE% = [2s(SA)/SA] x 100

Upper bound = SA + 2s(SA)

Lower bound = SA − 2s(SA)

These sample statistics provide current information on C. fumiferana, which can be used to 

estimate the potential for defoliation and damage in the future. A hazard rating and treatment 

priority can be determined for the stand using these sample statistics as described below 

under “Estimating Egg mass Density in a Forest Management Unit.”

Estimating Egg Mass Density in a Forest Management Unit: Sampling all stands within the 

forest management unit allows a manager to determine if harvest is needed and, if so, which 

stands should be harvested first. Calculate egg mass density and associated sample statistics 

for each stand sampled using the procedures described above. Continue by determining the 

hazard rating and treatment priority for the unit using the procedures described below.

1. Rank each stand based on the stand’s vulnerability to spruce budworm damage. These 

hazard ratings are based on the particular system used, such as Batzer and Hastings (1981) 

for Minnesota and Lynch et al. (1984) for the Upper Peninsula of Michigan.
138

Rank of stand Vulnerability rating

1 Low

2 Moderate

3 High

4 Extreme

2. Rank each stand based on the defoliation of current year foliage (Montgomery et al. 1982):

Rank of stand Defoliation of current-year foliage % Defoliation

1 None; defoliation minimal or not observed 0–20%

2 Light to moderate 21–50%

3 Heavy 51% or more but no evident topkill

4 Severe 51% or more; topkill evident

3. Rate each stand equal to the number of successive years visible defoliation has occurred.

4. Add +2 if the spruce budworm population is increasing or −2 if the population is declining. 

5. Rate each stand based on the estimated future defoliation based on the following:

Average egg mass density Expected defoliation

Rating

Pure Fir or 

mixed spruce fir Pure Spruce Percent Intensity

1 <0.4 <0.8 <26 Light 

2 0.4–1.09 0.8–2.19 26–50 Moderate

3 1.1–1.7 2.2–3.4 51–75 Heavy

4 >1.7 >3.4 >75 Severe

The level of expected defoliation on white spruce should be lower than defoliation on balsam 

fir for similar egg mass densities, so the critical values for average egg mass density are 

raised for stands of pure spruce. 

Sum the five ratings described above for each stand to determine the treatment priority for the 

stand. Stands rated as “high” should be harvested immediately. Harvest can be postponed 

2–3 years for stands rated as “intermediate”, while stands rated as “low” should be monitored 

for a change in treatment priority until harvested.

Treatment priority Sum of 5 ratings

1= low 2–7

2 = intermediate 8–11

3 = high 12+
139

Notes

Not all stands in a forest management unit need to be sampled for C. fumiferana. Egg mass 

density can be estimated from at least half of the stands in the unit or from at least five 

stands; managers should choose whichever is greater in number. The guidelines for sampling 

a limited number of stands within a forest management unit, instead of sampling all stands 

in the unit, are based on the authors’ experiences. Refer to the original publication for the 

formulae used to calculate the sample statistics for a management unit if sampling a selected 

number of stands instead of all stands.

Refer to the original publication for detailed information regarding the calculations 

and formulae used with these methods, and for methods used to monitor C. fumiferana

populations over larger regions. The authors also suggest that the procedures used for egg 

mass estimates could be adopted for use with sampling overwintering larvae and pupae from 

foliage samples, or sampling adult moths using light/pheromone traps.

References

Batzer, H. O.; Hastings, A. R. 1981. Rating spruce-fir stands for spruce budworm vulnerability in Minnesota. In: Hedden, R. L.; Barras, S. J.; Coster, J. E., technical coordinators. 

Hazard-rating systems in forest insect pest management. Gen. Tech. Rep. WO-27. Washington, D.C.: U.S. Department of Agriculture, Forest Service; p. 105-108.

Lynch, A. M.; Fowler, G. W.; Witter, J. A. 1984. Development of empirical models to rate 

spruce-fir stands in Michigan’s upper peninsula for hazard from the spruce budworm 

(Lepidoptera: Tortricidae): a case history. Great Lakes Entomologist 17: 163-174.

# Montgomery, B. A.; Simmons, G. A.; Witter, J. A.; Flexner, J. L. 1982. The spruce budworm handbook: a management guide for spruce-fir stands in the Lake States. Handb. 82-

7. Ann Arbor, MI: Michigan Cooperative Forest Pest Management Program; 35 p
140

Table 1. Field data sheet for recording data to estimate stand eff mass density.

Stand no. or name:

Plot no. Tree no. Branch no.

Foliage measurements 

(cm)

No. of new egg masses

Length Maximum 

width

Total Parasitized

1

1 1

2

2 1

2

3 1

2

2

1 1

2

2 1

2

3 1

2

3

1 1

2

2 1

2

3 1

2

4

1 1

2

2 1

2

3 1

2

5

1 1

2

2 1

2

3 1

2
141

Table 2. Data sheet for recording data to estimate a searcher’s accuracy proportion.

Bag 

no.

No. egg masses found Bag 

no.

No. egg masses found Bag 

no.

No. egg masses found

Searcher Checker Total Searcher Checker Total Searcher Checker Total

1 18 35

2 19 36

3 20 37

4 21 38

5 22 39

6 23 40

7 24 41

8 25 42

9 26 43

10 27 44

11 28 45

12 29 46

13 30 47

14 31 48

15 32 49

16 33 50

17 34

 Total 

P = Searcher acciracy proportion

S = Total number of egg masses found by searcher

SC = Total number of egg masses found by searcher and checker

P = S/SC = = 
142

Spruce Budworm

Choristoneura fumiferana (Clemens)

Lepidoptera: Tortricidae

MacLean, D. A.; MacKinnon, W. W. 1998. Sample sizes required to estimate defoliation of 

spruce and balsam fir caused by spruce budworm population accurately. Northern Journal 

of Applied Forestry 15: 135-140. 

Objective

To determine the sampling intensity required to predict C. fumiferana defoliation accurately 

as a function of host tree species. 

Abstract

Spruce budworm, Choristoneura fumiferana (Clemens), is the most destructive defoliator 

of balsam fir, Abies balsamea (L.) Mill., and white spruce, Picea glauca (Moench) Voss, in 

eastern North America. The last three larval instars cause most of the defoliation. Periodic 

outbreaks occur every 30 years, with epidemics lasting 5–10 years. 

An assessment of 172,300 individual shoots from 6,890 mid-crown branches taken from 135 

spruce and balsam fir stands throughout New Brunswick was conducted to determine sample 

sizes required for estimating C. fumiferana defoliation. Dominant host species were white 

spruce, red spruce (P. rubens Sarg.), and black spruce [P. mariana (Mill.) B.S.P.], as well 

as balsam fir. Host species and the mean defoliation level, as classified with a preliminary 

assessment, determine the required sample size of shoots per branch or branches per stand 

needed to predict budworm defoliation. Sample sizes were estimated at the 90 or 95% probability level that the confidence interval was within ±10% defoliation as a function of average 

defoliation level and tree species. For a desired probability level of 90%, 17–19, 24–37, and 

44–58 shoots per branch should be sampled when mean defoliation is light, moderate, and 

severe, respectively. For a desired probability level of 95%, 26–28, 67–88, and 44–56 shoots 

per branch should be sampled when mean defoliation is light, moderate, and severe, respectively. The mean number of branches required to estimate defoliation ranged from 7–24 per 

stand, depending on tree species and the desired probability level. Sampling took about 5–10 

min to collect mid-crown branches with an additional 10–30 sec per shoot to estimate defoliation.

Sampling Procedure

Select the desired probability level and appropriate tree species to be sampled from Table 1 

if sampling individual trees or from Table 2 if sampling stands. The minimum number of 
143

shoots per branch or branches per stand required for sampling each species is given in the 

‘light’ defoliation columns. Collect the appropriate minimum number of branches from randomly selected trees and rate the observed defoliation on the minimum number of shoots or 

branches into the following classes: 

Percent defoliation 

observed Defoliation level

0–30% Light

31–70% Moderate

71–100% Severe

This preliminary assessment provides a rough idea of the defoliation on a tree or within the 

stand and can be used to eliminate unnecessary sampling. 

Sampling individual trees: Reference Table 1 to determine the required number of shoots 

to assess on each branch given the desired probability level, tree species, and mean defoliation level determined from the preliminary assessment of the minimum number of shoots. 

Using pole pruners, remove one whole branch sample from the mid-crown of randomly selected, dominant or co-dominant trees. Assess defoliation only on current year foliage, moving from the tip of the branch to the base until the required number of shoots on a branch has 

been processed. In general, sampling a whole branch will provide sufficient shoots for classifying individual trees, but a second mid-crown branch from the same tree may be necessary 

if few shoots are present on the first branch. Classify the tree according to the percentage of 

observed defoliation as described above. 

Sampling stands: Reference Table 2 to determine the required number of branches to assess 

within a stand given the desired probability level, tree species, and mean defoliation level 

determined from the preliminary assessment of the minimum number of branches. Using 

pole pruners, remove one whole branch sample from the mid-crown of randomly selected, 

dominant or co-dominant trees in each stand. Sample only one branch from each tree (i.e., 

the number of branches required is also the number of trees to be sampled). If the average 

defoliation level from the initial sample of the minimum number of branches is moderate or 

severe, reference Table 2 to determine the additional number of whole, mid-crown branch 

samples required. Assess defoliation only on current-year foliage. Classify the stand according to the percentage of defoliation observed on the branches as described above. 

Notes

The effort required to process the full range of shoots or branches justifies the recommended 

practice of first sampling the minimum number of required shoots or branches to classify 

the stand as having light, moderate, or severe defoliation. Increasing the probability level 

from 90 to 95% increases the sampling effort by 50%. This sampling plan assumes that no 

backfeeding occurred and all defoliation within an age class resulted from feeding during the 

year the foliage was produced. Red and black spruces were combined into a single class for 
144

this study because they hybridize readily. No red-black spruce stands with severe defoliation 

were sampled in this study. Managers should select the dominant susceptible species in each 

stand as the host species in Tables 1 and 2.

Table 1. Mean number of shoots per branch required to estimate spruce budworm defoliation 

with 90 or 95% probability that the 95% confidence interval is within ±10% defoliation, as a 

function of average defoliation level and tree species.

Average Defoliation Levela

Variable Species Light Moderate Severe Totalb

A. No. of observationsc Balsam fir 1,610 970 395 2,975

White spruce 757 470 96 1,323

Red-black spruce 2,208 361 25 2,594

B. Required no. of 

shoots per branch 

with 90% probabilityBalsam fir

19 44 24 29

White spruce 18 45 27 30

Red-black spruce 17 58 37 37

C. Required no. of 

shoots per branch 

with 95% probabilityBalsam fir

28 67 36 44

White spruce 27 68 40 45

Red-black spruce 26 88 55 56

a Defoliation classes for calculating sample sizes were defined as light 0-30%, moderate 31- 

70%, and severe 71- 100%.

b

 Total values are sums for number of observations and means per species for required num

ber of shoots per branch. c Number of branches sampled per species and defoliation level, with individual ratings of 

defoliation on 25 shoots per branch.
145

Table 2. Mean number of branches per stand required to estimate spruce budworm defoliation with 90 or 95% probability that the 95% confidence interval is within ±10% defoliation, 

as a function of average defoliation level and tree species.

Average Defoliation Levela

Variable Species Light Moderate Severe Totalb

A. No. of observationsc Balsam fir 165

156 38 359

White spruce 30 31 3 64

Red-black spruce 295 31 0 326

B. Required no. of 

shoots per branch 

with 90% probabilityBalsam fir 9 24 13 15

White spruce 8 17 10 12

Red-black spruce 7 19 – 13

C. Required no. of 

shoots per branch 

with 95% probabilityBalsam fir 14 39 21 25

White spruce 13 26 15 18

Red-black spruce 10 30 – 20

a Defoliation classes for calculating sample sizes were defined as light 0-30%, moderate 31-70%, and 00%. b

 Total values are sums for number of observations and means per species for required number of branches per stand.

c Number of stands sampled per species and defoliation level, with ≥5 mid-crown branches sampled per stand.

Tables 1 and 2 reprinted with permission from the Society of American Foresters, granted June 9, 

2009. 
146

Spruce Budworm

Choristoneura fumiferana (Clemens)

Lepidoptera: Tortricidae

Moody, B. H.; Otvos, I. S. 1980. Distribution of hibernating spruce budworm larvae within 

crowns of balsam fir trees in Newfoundland. Information Rpt. N-X-182. St. John’s, Newfoundland: Environment Canada, Forestry Service, Newfoundland Forest Research Centre; 

21 p. 

Objective

To determine the appropriate crown level for sampling C. fumiferana larvae overwintering in 

balsam fir trees.

Abstract

Spruce budworm, Choristoneura fumiferana (Clemens), is the most destructive defoliator 

of balsam fir, Abies balsamea (L.) Mill., and white spruce, Picea glauca (Moench) Voss, in 

eastern North America. The last three larval instars cause most of the defoliation. Periodic 

outbreaks occur every 30 years, while epidemics can last 5–10 years.

Estimates of second instar C. fumiferana are used to predict the risk of defoliation the subsequent year. A study was carried out in two stands of immature balsam fir in Newfoundland to 

determine the distribution pattern of overwintering second instars. The results indicated that 

a high proportion of larvae hibernated in the mid-crown of balsam fir, which is the most appropriate sampling universe for this larval stage. Only 5 and 18% of the second instars were 

found on the main stem and internodal branches, respectively. Entire nodal (whorl) branches 

cut at 2.5 cm from the trunk should be sampled, as only 36% of the larvae found on branches 

from the mid-crown occurred within the apical 45 cm of the branch. Sampling efficiency, 

expressed as coefficients of variation, were lower for the upper and middle crowns than the 

lower crown. 

Sampling Procedure

Randomly select and remove whole nodal branches from the mid-crown of balsam fir. 

Branches should be cut 2.5 cm from the trunk. Count and record the number of second instars on the entire branch. The branch surface area (calculated as the total length of branch X 

the width at half the total length) should be determined if larval density is to be expressed on 

a per-hectare basis. Either measure all sampled branches or subsample a smaller number of 

branches to estimate a mean branch area for each plot.
147

Notes

This study is based on 5 randomly selected immature balsam firs from two locations in Newfoundland. As such, the distribution of second instar C. fumiferana reported by the authors 

may not represent distribution of instars on other host trees or in other regions in North 

America. The authors did not suggest how many branches should be removed from each tree 

for practical sampling, but they selected two branches from the mid-crown for their study.
148

Spruce Budworm

Choristoneura fumiferana (Clemens)

Lepidoptera: Tortricidae

Montgomery, B. A.; Simmons, G. A.; Witter, J. A.; Flexner, J. L. 1982. The spruce budworm 

handbook: a management guide for spruce-fir stands in the Lake States. Handb. 82-7. Ann 

Arbor, MI: Michigan Cooperative Forest Pest Management Program; 35 p. 

Objectives

To provide a summary of sampling plans for adults, larvae, pupae, and egg masses of C. 

fumiferana for stands of white spruce and balsam fir in the Great Lakes region; to describe 

methods of estimating defoliation in this region.

Abstract

Spruce budworm, Choristoneura fumiferana (Clemens), is the most destructive defoliator 

of balsam fir, Abies balsamea (L.) Mill., and white spruce, Picea glauca (Moench) Voss, in 

eastern North America. The last three larval instars cause most of the defoliation. Periodic 

outbreaks occur every 30 years and epidemics can last 5–10 years.

Methods used to sample C. fumiferana in the Great Lakes region of the US are summarized 

as a cooperative effort between the U.S. Department of Agriculture and the Canadian Department of the Environment. Detailed information on sampling all life stages of C. fumiferana, 

as well as aerial surveys of feeding damage, are presented in the handbook. Such methods 

allow forest managers to monitor endemic populations of C. fumiferana and detect increasing 

densities before defoliation and tree mortality occurs.

Sampling Procedure

Aerial survey of defoliation: Conduct surveys in late July when infested foliage appears 

reddish-brown. At a height of 305 m, fly east and west lines 4.8 km apart. Use 1:50,000 

scale maps to delineate areas of infestation and measure the acreage with a dot grid. Determine the level of defoliation and stand condition using the following codes established by the 

USDA Forest Service, State and Private Forestry:
149

Defoliation Stand Condition

L Light to moderate, 0–50% of crown showing 

red-brown discoloration

No tree mortality or top kill

H Heavy, 51–100% of crown showing red-brown 

discoloration

No tree mortality or top kill

S Severe, 51–100% of crown showing red-brown 

and gray discoloration

Follow the aerial survey with a ground check of 10% of the heavily or severely defoliated 

areas to verify that C. fumiferana produced the observed damage and that the aerial mapping 

was accurate.

Ground assessment of defoliation using the Fettes Method: This technique is generally 

used with a sampling method for insects. In July and August, cut branches from the midcrown of white spruce or balsam fir trees. Visually estimate the percentage of defoliation 

(needles removed) from each current-year shoot on each branch (Fig. 6). Average the percentage of defoliation of the current-year shoots to obtain an overall percentage of defoliation 

for the branch. Determine the level of defoliation using the following categories: 

Defolation level Percent defoliation

Trace 0–5

Low 6–20

Moderate 21–50

High 51–80

Severe 81–100+

Binocular assessment of defoliation: This technique is used independently of insect populations. Evaluate stand defoliation of spruce and fir trees using binoculars. Rank the green 

(live foliage) portion of the crown using the following categories:

1 No defoliation: no observable feeding damage, 0–20% of total foliage missing.

2 Light to moderate defoliation: 21–50% defoliation of total foliage.

3 Heavy defoliation: 51% or greater defolation with no observable top-kill.

4 Severe defoliation: 51% or greater defoliation with obvious top-kill.

Sampling pupae: Sampling should not be conducted until all larvae have pupated (late June 

or early July) but can continue through moth emergence (August) by counting pupal cases. 

However, empty pupal cases can be dislodged from branches so sampling earlier will provide 

a more accurate estimate of population density. Pupal sampling requires less time and is less 

expensive than sampling egg masses. 

Randomly select two dominant or co-dominant balsam fir or white spruce trees at each of five 

collection sites in a stand. Clip one branch at least 45-cm long from the upper mid-crown 
150

of both trees. Use pole pruners with a collection basket below the cutting head to catch the 

branches. Examine each branch and tally the number of pupae and empty pupal cases on the 

apical 45-cm length. Compare the cumulative pupal density to Table 3 to classify the population of C. fumiferana in the stand as high or low densities.

Sampling adults: As of 1982, pheromone traps had been tested but not used operationally in 

the Great Lakes region. Test trials showed that traps baited with C. fumiferana sex pheromone effectively attracted males even when larval densities were below 1 larva per branch. 

Prioritize stands for sampling, taking into consideration stand accessibility, value, and risk 

rating. Establish 1 to 5 permanent sampling sites in 25 to 50% of the stands identified as 

having the highest risk. Deploy traps baited with synthetic spruce budworm pheromone at 

the permanent sampling sites in early summer, before larvae pupate. Hang traps on branches 

2 m above ground with no branches or foliage within 30 cm of each trap to open the air space 

(trim foliage if needed). Leave traps undisturbed from late June through July until the flight 

period is completed and then tally the number of captured moths in each trap. Plot the total 

trap catch over years to detect increasing densities. Large increases in the number of trapped 

moths over several consecutive years may signal an incipient outbreak of C. fumiferana

within 3–5 years and precautions should be considered to limit tree damage and mortality.

Sampling egg masses: Egg mass surveys are more time-consuming and expensive than sampling pupae. Sampling should begin after adult moths are no longer present (usually August) 

and can continue through September. Randomly select 1 to 3 collection sites for each 16-ha 

stand. At each collection site, use pole pruners to cut three 38-cm mid-crown branches from 

each of 3 balsam firs or 5 white spruces. Trees should be dominant or co-dominant. Count 

the number of current-year egg masses (i.e., round, green/white egg masses) on each branch. 

Do not include old egg masses that appear flat and gray or parasitized egg masses, which appear black. Calculate the mean number of current-year egg masses per branch and use Table 

4 to predict the defoliation level expected for that stand the following summer.

Sampling small larvae (second instars): This procedure is a useful alternative if egg mass 

surveys are limited by available resources. Sample second instars between September and 

April in most areas. Using pole pruners, cut several 45-cm branch tips randomly through the 

upper mid-crown of several dominant balsam firs or white spruces. Trees should be widely 

distributed. Allow branch samples to thaw overnight in the laboratory before cutting them 

into 5–10 cm pieces. Place pieces in a wire basket and submerge into a bucket containing 9 

liters of 2% aqueous solution of sodium hydroxide between 49-60°C (120-140°F) overnight. 

Agitate the soaked branch pieces and collect all needles and bark scales into labeled collection jars. Filter the contents of each jar through sieves and a separation funnel. Add hexane 

to the oil/water interface where most of the larvae collect and vacuum filter the larvae onto 

gridded filter paper. Using a stereomicroscope, count the number of larvae on the filter paper 

to determine the number of larvae per branch. Refer to Sanders (1980) for more detailed 

information regarding this procedure. 
151

Notes

These procedures are established for balsam fir and white spruce in upper Minnesota and 

Wisconsin as well as Michigan’s Upper Peninsula. As such, these procedures may not be 

effective for other host trees or in other geographical regions. The authors did not specify 

the number of branch samples per tree or the number of trees to be sampled when sampling 

second instars. Do not expose hexane to flame or another heat source as it is an explosive, 

volatile chemical.

References

* Sanders, C. J. 1980. A summary of current techniques used for sampling spruce budworm 

populations and estimating defoliation in eastern Canada. Rep. 0-X-306. Canadian Forest 

Service, Great Lakes Forestry Centre; 33 p.

Number of sampled 

branches per stand

Population category

Low High

(Cumulative pupae)

2 – 33 or more

4 1 or less 50

6 5 66

8 10 83

10 14 100

The same classification is also currently being used for white spruce.

Table 4. Defoliation predictions based on the average number of egg masses per 15-in. (38-

cm) balsam fir branch.

Average number of egg 

masses/branch/stand

Expected defoliation

% Intensity

Less than 0.2 less than 26 Light 

0.2–0.5 26–50 Moderate

0.5–0.9 51–75 Heavy

more than 1.0 more than 75 Severe

Less defolation would be expected for similar egg mass density levels on white spruce.
152

Figure 6. Fettes method of estimating defoliation.
153

Spruce Budworm

Choristoneura fumiferana (Clemens)

Lepidoptera: Tortricidae

Morris, R. F. 1954. A sequential sampling technique for spruce budworm egg surveys. Canadian Journal of Zoology 32: 302-313.

Objective

To develop a sequential sampling plan for C. fumiferana egg mass surveys.

Abstract

Spruce budworm, Choristoneura fumiferana (Clemens), is the most destructive defoliator 

of balsam fir, Abies balsamea (L.) Mill., and white spruce, Picea glauca (Moench) Voss, in 

eastern North America. The last three larval instars cause most of the defoliation. Periodic 

outbreaks occur every 30 years, while epidemics can last 5–10 years.

A sequential sampling plan was developed for C. fumiferana egg mass surveys in eastern 

Canada based on whether pole pruners can be used to sample the mid-crown of balsam fir, 

Abies balsamea (L.) Mill., within a stand. Trees selected for sampling are felled if the overall 

stand height is too tall for sampling the mid-crown with pole pruners (Method I). Fewer trees 

are needed to classify the egg mass density within a stand using Method I than if mid-crown 

branches are sampled using pole pruners (Method II), but felling trees requires more effort. 

A maximum of 10 or 15 trees are sampled with Methods I and II, respectively. With Method 

II, as few as 1 or 2 branches need be sampled per plot when C. fumiferana populations are 

extremely low or high (Table III). Field sampling parties preferred Method II due to ease of 

making collections without sacrificing accuracy.

Egg mass density is estimated by sampling multiple branches sampled from the crown 

divided into quarters with Method I, and by sampling one branch from the mid-crown with 

Method II. With both methods, egg mass density is expressed as the number of egg masses 

per 9.29 m2

. Light, moderate, and severe infestations are defined as <25, 50–100, and >200 

egg masses per 9.29 m2. If, after sampling the maximum number of branches per plot, the 

cumulative number of egg masses falls between the light and moderate bands, or between the 

moderate and severe bands, then infestation is classified as either light-moderate or moderatesevere, respectively.

Sampling Procedure

Begin sampling after the oviposition period has ended. Sampling can continue into egg hatch 
154

provided that workers can distinguish between old and new egg masses. Randomly select 

trees in each plot. If the trees in a stand are too tall to reach the mid-crown with pole pruners, 

then trees selected for sampling are felled (Method I). From each felled tree, sample a whole 

branch from both the apical and second quarters of the crown, and sample a half of a branch 

from the third and basal quarters of the crown. This division distributes sampling intensity 

throughout the tree, but pool branch samples to provide a single mean density for the tree. 

Discard the nonfoliated basal stem of each whole branch collected. If the mid-crown of the 

selected trees can be reached using pole pruners, then sample a single whole branch from the 

mid-crown (Method II). 

With whole branches collected with either method, measure the length of the foliated stem 

and the width at the midpoint of the stem. Multiply the two measurements to obtain a crude 

estimate of the foliated area of each branch. For half branches collected with Method I, 

divide each half branch longitudinally by removing all lateral branches along one side of the 

main stem. Count the number of healthy egg masses on each foliated branch half and express as the number of egg masses per 9.29 m2

 of foliage surface. Pool counts for branches 

collected with Method I to provide a single mean density for each tree.

Reference Table III for each pooled sample for a tree if using Method I or after processing each branch if using Method II. Continue sampling until the cumulative number of egg 

masses falls into one of the zones (i.e., light, moderate, or severe), then stop and classify the 

infestation level accordingly. 

The general expected egg mass density found during light, moderate, and severe infestations 

are as defined below:

No. of egg masses per 9.29 m2 Infestation

≤ 25 Light 

50–100 Moderate

> 200 Severe

If the cumulative count falls within either the light-moderate or the moderate-severe bands 

after sampling the maximum number of branches per plot, classify the infestation as either 

light-moderate or moderate-severe.

Notes

Workers should have experience in distinguishing between old and new egg masses and 

between healthy and parasitized or predated egg masses. Branches should be re-examined at 

least partially by a second worker to check that egg masses are counted correctly. Only one 

method should be used to classify the infestation level within a stand.
155

Table III. Sequential table for use by field parties, read from the acceptance and rejection 

lines (Figs. 1 and 2, in original publication). (Sampling is continued until the cumulative 

result falls outside the bands into one of the zones.)

Tree

Cumulative no. of egg masses per 9.29 m2

Light vs. moderate band Moderate vs. severe

Sampling Method I

1

LIGHT ZONE

8 – 59

MODERATE ZONE

42 – 225

SEVERE ZONE

2 42 93 183 366

3 78 129 323 506

4 112 163 460 643

5 147 198 599 782

6 182 233 738 921

7 216 267 878 1061

8 251 302 1016 1199

9 286 337 1154 1337

10 320 371 1291 1474

Sampling Method II

1

LIGHT ZONE

0 – 90

MODERATE ZONE

0 – 360

SEVERE ZONE

2 5 126 35 498

3 41 162 174 637

4 77 198 312 775

5 110 231 452 915

6 145 266 591 1054

7 179 300 728 1191

8 215 336 868 1331

9 249 370 1005 1468

10 284 405 1144 1607

11 318 439 1283 1746

12 355 476 1420 1883

13 389 510 1559 2022

14 422 543 1698 2161

15 457 578 1837 2300

Table III reprinted with permission from NRC Research Press, granted June 8, 2009. 

(© 2008 NRC Canada or its licensors. Reproduced with permission.)
156

Spruce Budworm

Choristoneura fumiferana (Clemens)

Lepidoptera: Tortricidae

Morris, R. F. 1955. The development of sampling techniques for forest insect defoliators, 

with particular reference to the spruce budworm. Canadian Journal of Zoology 33: 225-

294.

Objective

To review the development of sampling techniques for C. fumiferana.

Abstract

Spruce budworm, Choristoneura fumiferana (Clemens), is the most destructive defoliator 

of balsam fir, Abies balsamea (L.) Mill., and white spruce, Picea glauca (Moench) Voss, in 

eastern North America. The last three larval instars cause most of the defoliation. Periodic 

outbreaks occur every 30 years, while epidemics can last 5–10 years.

This publication represents an exhaustive compendium of sampling techniques for C. 

fumiferana that evolved during the classic “Green River” ecological study of the population 

dynamics of this defoliator and its natural enemies in New Brunswick, Canada. These methods represent one of the first rigorous sampling plans refined for a defoliating forest pest and 

were the basis for the life table for C. fumiferana constructed by Morris and Miller (1954). 

The table of contents for the article is presented here; interested readers should review the 

publication for detailed information regarding the theory and mechanics of sampling forest 

defoliators.
157

Sampling Procedure

THE DEVELOPMENT OF SAMPLING TECHNIQUES

FOR FOREST INSECT DEFOLIATORS, WITH PARTICULAR

REFERENCE TO THE SPRUCE BUDWORM

By R. F. Morris

TABLE OF CONTENTS

Page

Abstract............................................................................................. 226

1. Introduction................................................................................. 226

2. The expression of population............................................................. 227

3. The objects of sampling................................................................... 229

4. The timing of sampling.................................................................... 230

 4.1 General considerations........................................................... 230

 4.2 The spruce budworm............................................................ 231

 4.3 Retention of egg masses on the foliage........................................ 234

 4.4 Retention of pupal cases on the foliage....................................... 235

5. The mechanics of sampling............................................................... 237

 5.1 Collecting the sample branches................................................. 237

 5.2 Examining the foliage............................................................ 239

 5.3 Checking the foliage............................................................. 240

6. Selection of the universe.................................................................. 242

7. Selection of the sample unit............................................................... 245

8. Representativeness in the tree............................................................ 247

 8.1 Distribution of budworm population........................................... 247

 8.2 Distribution of branch surface................................................ 250

 8.3 Representative sampling......................................................... 251

9. Representativeness in the stand........................................................... 253

 9.1 Distribution of population in a two-storied stand............................ 253

 9.2 Selection of trees in each story................................................. 256

10. Correlation between successive samples................................................ 258

11. Transformation of data..................................................................... 261

12. Optimum sample size..................................................................... 268

 12.1 Degree of precision required.................................................... 269

 12.2 Number of sample units per tree................................................ 271

 12.3 Number of trees per plot......................................................... 274

 12.4 Number of clusters per plot..................................................... 278
158

 12.5 Influence of population level................................................... 279

 12.6 The cost function................................................................ 282

13. Absolute population........................................................................ 285

14. Incidental data.............................................................................. 288

 14.1 Population intensity.............................................................. 288

 14.2 Life table data...................................................................... 289

 14.3 Other sampling systems.......................................................... 290

15. Discussion.................................................................................. 290

Acknowledgements............................................................................... 292

References.......................................................................................... 293

Reference

Morris, R. F.; Miller, C. A. 1954. The development of life tables for the spruce budworm. 

Canadian Journal of Zoology 32: 283-301. 

Table of Contents reprinted with permission from NRC Research Press, granted June 8, 

2009. (© 2008 NRC Canada or its licensors. Reproduced with permission.)
159

Spruce Budworm

Choristoneura fumiferana (Clemens)

Lepidoptera: Tortricidae

Régnière, J.; Lysyk, T. J.; Auger, M. 1989. Population density estimation of spruce budworm, Choristoneura fumiferana (Clem.) (Lepidoptera: Tortricidae) on balsam fir and 

white spruce from 45-cm mid-crown branch tips. Canadian Entomologist 121: 267-281. 

Objectives

To relate density of C. fumiferana on balsam fir to density on white spruce; to examine C. 

fumiferana density within host canopy; and to improve the accuracy of estimation through 

error corrections.

Abstract

Spruce budworm, Choristoneura fumiferana (Clemens), is the most destructive defoliator 

of balsam fir, Abies balsamea (L.) Mill., and white spruce, Picea glauca (Moench) Voss, in 

eastern North America. The last three larval instars cause most of the defoliation. Periodic 

outbreaks occur every 30 years, while epidemics can last 5–10 years.

Densities of C. fumiferana are often estimated from 45-cm branch tip samples taken from the 

mid-crown of host trees. This sampling method is used on both balsam fir and white spruce, 

but the appropriateness of comparing densities estimated by this technique between host 

species has not been evaluated previously. Foliage density in these branch samples is determined by branch architecture, which differs between white spruce and balsam fir. Research 

conducted in eastern Canada indicated that white spruce had foliage nearly 40% denser than 

balsam fir. Pest density should be expressed in a consistent manner to allow comparisons 

between host trees and among studies. Densities of C. fumiferana expressed as per sample, 

per m2 surface area, per kg fresh weight, and per foliage bud were compared but none of the 

expressions presented a clear advantage over the others in regards to reduction in variability. 

Fresh weight is generally easier to calculate than surface area, so the authors recommend 

expressing density of C. fumiferana per kg fresh foliage weight. 

The distribution of C. fumiferana within the host canopy differs with life stages. While changes 

in the vertical distribution of C. fumiferana did not appear to affect density estimates, changes 

in the horizontal distribution along branches are more important. Overwintering larvae prefer 

the base of branches over the apical tips where branch samples are taken. In contrast, the authors found more first through fourth instars (60%) and more egg masses (67%) within 45-cm 

of the branch tips. Density estimates of different life stages of C. fumiferana on 45-cm midcrown branch samples can be compared using a correction factor for each host species. 
160

Sampling Procedure

Sample 45-cm branch tips from the mid-crown of host trees using pole pruners. Use collection baskets attached to the head of the pole pruners when large instars are present as they 

tend to drop off branches when disturbed. Return samples to the laboratory and tally the 

number of C. fumiferana present in each life stage. Have samples randomly checked by experienced personnel for accuracy in detecting C. fumiferana. Weigh the fresh foliage present 

in the samples. Express the density of C. fumiferana in terms of mean kg fresh weight. If of 

interest, measure branch sample surface area so foliage density, an index of tree health, can 

be expressed as kg/m2. Use the method of Miller et al. (1971) to extract small overwintering 

larvae from the foliage using a caustic solution of sodium hydroxide (NaOH).

Corrections for changes in horizontal distribution along mid-branch samples and examination 

error by personnel processing the branch samples can be made simultaneously for each host 

species. Use the following equations to estimate C. fumiferana density on whole branches 

(d) from the density of C. fumiferana per kg fresh foliage weight estimated from 45-cm 

branch tip samples (d1

):

d = d1

 (u + [(1 – u)/–5.31 + 4.14a – 0.45 a2])(1 – exp[1.94 – 1.14a])–1 and

d = d1 (u + [(1 – u)/–1.80 + 1.88a – 0.21 a2])(1 – exp[1.94 – 1.14a])–1

for balsam fir and white spruce, respectively. In both equations, a = the average instar of C. 

fumiferana present. The variable u represents the proportion of foliage by weight present in 

the 45-cm branch tip samples taken from the mid-crown. Use the approximate values of u = 

0.47 and u = 0.30 for balsam fir and white spruce, respectively. However, u varies for both 

host species among stands, so consider estimating u in each sampled stand of balsam fir and 

white spruce for the most accurate value. 

If sampling egg masses or overwintering larvae, use the average value of a = 2 and set the 

examination error term of [1– exp(1.94 – 1.14a)]–1 to 1 as the authors did not determine the 

examination error for these life stages. 

When branch samples are examined for accuracy by a second experienced observer, the correction factor for the examination term, which is [1– exp(1.94 – 1.14a)]–1, can be eliminated 

from the equations.

Notes

The simultaneous correction factor should not exceed 1.5 as values larger than this were not 

examined in this study.
161

Reference

Miller, C. A.; Kettela, E. G.; McDougall, G. A. 1972. A sampling technique for overwintering 

spruce budworm and its applicability to population surveys. Inf. Rep. M-X-25. Fredericton, N.B.: Canadian Forest Service; 11 p.
162

Spruce Budworm

Choristoneura fumiferana (Clemens)

Lepidoptera: Tortricidae

Sanders, C. J. 1988. Monitoring spruce budworm population density with sex pheromone 

traps. Canadian Entomologist 120: 175-183.

Objective

To evaluate the use of pheromone traps to monitor changes in C. fumiferana populations.

Abstract

Spruce budworm, Choristoneura fumiferana (Clemens), is the most destructive defoliator 

of balsam fir, Abies balsamea (L.) Mill., and white spruce, Picea glauca (Moench) Voss, in 

eastern North America. The last three larval instars cause most of the defoliation. Periodic 

outbreaks occur every 30 years, while epidemics can last 5-10 years. 

Pheromone traps are extremely useful in monitoring adult populations of C. fumiferana as 

rising trap catches can indicate a potential outbreak. Trap catches were positively correlated 

to larval densities over a 21-year period in Ontario (r2 = 81%). In general, land managers should consider their management options when trap catches increase over 3 successive 

years or when >50 moths are captured per trap. Branch sampling may pinpoint where control 

options should best be applied in the near future as larval populations increase. Pheromone 

traps are now used as an early warning system for increasing densities of C. fumiferana in 

eastern North America.

Sampling Procedure

Use high-capacity traps such as Uni-traps (International Pheromone Systems, Wirral, U.K.), 

which are less likely to become saturated with captured adults. Sticky traps can be used in 

areas that generally have had low densities, but with the risk of trap saturation with increasing population density. Bait traps with the lure recommended for the current year. Lures 

with a very low potency (0.0003% [w/w]) are recommended as they remain effective in attracting male moths but do not lead to trap saturation. Traps should include a killing agent to 

immobilize attracted male moths. 

Deploy traps in stands with at least 50% balsam fir and/or white spruce 1–2 weeks before the 

adult flight period. In the center of each stand, hang traps 2 m above ground in a triangular 

cluster with 20 m between each trap. Leave traps in place until the flight period has ended, 

then collect traps and count the number of moths in each trap. Land managers should consid-
163

er their management options when trap catches increase over 3 successive years or when >50 

moths are captured per trap. Traps should be set out each year to monitor population trends 

of C. fumiferana over time and provide an early warning of potential outbreak densities.

Note

Land managers may consider initiating branch sampling when trap catches reach this “early 

warning threshold” as larval densities sufficiently high to warrant control measures may still 

not occur for several seasons. Branch sampling may pinpoint where control options should 

best be applied in the near future as larval populations increase.
164

Spruce Budworm

Choristoneura fumiferana (Clemens)

Lepidoptera: Tortricidae

Sanders, C. J. 1996. Guidelines for using sex pheromone traps to monitor eastern spruce 

budworm population densities. Frontline Tech. Note No. 94. Sault Ste. Marie, ON: Natural 

Resources Canada, Canadian Forest Service, Great Lakes Forestry Centre; 4 p. 

Objectives

To describe guidelines for the use of pheromone traps in monitoring low densities of C. 

fumiferana; to predict subsequent larval densities based on trap catch of male moths.

Abstract

Spruce budworm, Choristoneura fumiferana (Clemens), is the most destructive defoliator 

of balsam fir, Abies balsamea (L.) Mill., and white spruce, Picea glauca (Moench) Voss, in 

eastern North America. The last three larval instars cause most of the defoliation. Periodic 

outbreaks occur every 30 years, while epidemics can last 5–10 years. 

Pheromone traps are extremely useful in monitoring adult populations of C. fumiferana, 

especially when populations are low. Rising trap catches can indicate a potential outbreak. 

Furthermore, trap catch of adult males can be used to predict subsequent larval densities 

using the equation Log10(y+1) = –1.454 + 1.440(x+1), where x = moth catch and y = larvae per 10 m2

 foliage (r2 = 0.48). Using this formula, a trap catch of 100 moths corresponds 

to subsequent larval densities of 25 larvae per 10 m2

 foliage, or approximately 3 larvae per 

branch. Land managers should consider initiating larval sampling at this threshold, but continue monitoring adult populations using pheromone traps below this threshold. 

Sampling Procedure

Use Multi-pher (le Groupe Biocontrôle, Ste-Foy, Québec) or Uni-traps (International Pheromone Systems, Wirral, U.K.) baited with the recommended lures for the current year. Traps 

should include a killing agent to immobilize male moths attracted to the lure. 

Deploy traps before the adult flight period and leave in place until the flight period has ended. 

Traps should be placed in mature forest consisting of at least 50% white spruce and/or balsam fir of at least 10 ha in area. The recommended trap layout consists of three traps in a 

triangle evenly spaced at least 40 m apart and at least 40 m from the edge of the stand. Hang 

traps on dead branches at eye level at least 50 cm from the trunk. 
165

Collect traps after the flight period has ended and count the number of moths. Use the following equation to estimate the density of the subsequent larval population:

Log10(y+1) = –1.454 + 1.440(x+1)

where x = moth catch and y = larvae per 10 m2

 foliage. A trap catch of 100 moths corresponds to subsequent larval densities of 25 larvae per 10 m2

 foliage, or approximately 3 larvae per branch. Larval sampling at this density becomes practical, so managers can use this 

threshold as a guideline of when to begin sampling second instar C. fumiferana. Continue 

monitoring adult populations with pheromone traps below this threshold.

Notes

The formula given in the original publication is incorrect. The correct formula, Log10(y+1) 

= –1.454 + 1.440(x+1) (D. B. Lyons, NRC Canadian Forest Service, Great Lakes Forestry 

Centre, Ontario, pers. comm.), is provided in this summary.

Traps can be reused over several years, but the plastic will absorb some of the pheromone. 

As the pheromone of one insect pest may be an inhibitor for another species, traps should 

always be reserved for the same species. These recommendations are based on research 

conducted in mature mixed stands in the Great Lakes region. These recommendations may 

not accurately reflect other forest types in different locations and should be used with caution 

until validated for other regions.
166

Spruce Budworm

Choristoneura fumiferana (Clemens)

Lepidoptera: Tortricidae

Witter, J. A.; Lynch, A. M. 1985. Spruce budworms handbook: rating spruce-fir stands for 

spruce budworm damage in eastern North America. Agric. Handb. 636. Washington, 

D.C.: U.S. Department of Agriculture, Forest Service; 22 p. 

Objective

To describe several hazard-rating systems for C. fumiferana that provide information for 

short-term management decisions.

Abstract

Spruce budworm, Choristoneura fumiferana (Clemens), is the most destructive defoliator 

of balsam fir, Abies balsamea (L.) Mill., and white spruce, Picea glauca (Moench) Voss, in 

eastern North America. The last three larval instars cause most of the defoliation. Periodic 

outbreaks occur every 30 years, while epidemics can last 5–10 years.

The hazard-rating systems for C. fumiferana summarized in this handbook are a compilation resulting from a cooperative effort between the U.S. Department of Agriculture and the 

Canadian Department of the Environment (CANUSA). Ground-based and aerial sampling 

procedures allow forest managers to identify high-risk areas suitable for protective spraying 

or salvage operations using short-term (1–3 year) management decisions. 

Sampling Procedure

Ground-based sampling (based on Trial and Devine 1983): Conduct aerial surveys over 

spruce-fir stands in July and August and map the extent of defoliation over regional areas. 

Sample egg masses or overwintering second instars in August and September over widespread areas (1,000+ sampling sites used in Maine, 1982). Cut one branch from each of three 

dominant or codominant fir trees at each sampling site. Estimate the number of fresh egg 

masses or second instar C. fumiferana per 9.3 m2 (100 sq. ft.) from the three branch samples. 

Estimate the level of defoliation of the current year and the previous year, as well as tree 

vigor, from these branch samples. In early fall, calculate a hazard-rating value for each stand 

or sample site using Table 1 and summing the values for each parameter included in the table. 

Develop a budworm population prediction map using the egg mass or second instar densities. Develop a composite hazard map using the individual stand hazard values generated 

from Table 1. Select spray or salvage areas for next year based on the composite hazard map 

while considering the availability of resources and weighing other inputs (i.e., social, politi-
167

cal, and economic conditions). 

Aerial sampling system (based on McCarthy et al. 1983): Take color photographs (35- or 70-

mm) with stereo overlap from fixed-wing, light aircraft over spruce-fir stands. Interpret stand 

defoliation, mortality, density, and proportion of host species from the photos using Table 2. 

Rank crown defoliation as follows:

1 = 0–20% 

2 = 21–50%

3 = 51% or more without topkill

4 = 51% or more with topkill

Compute the average tree defoliation rank for a stand and compare to the overall class label 

in Table 2. Calculate the percent mortality of host tree species in the stand and assign a class 

rank. Describe each stand as open, average, or dense. Calculate the proportion of stand comprised of host tree species and assign a class rank according to stand density. Finally, determine the overall stand hazard-rating value by summing the class ranks described above and 

comparing to Table 2. The hazard-rating value reflects the relative probability that a particular stand will be attacked and damaged by C. fumiferana during the next several years.

Notes

The ground-based sampling procedure is based on practices conducted in Maine in 1982. 

For more information, see Trial and Devine 1983. Olsen et al. (1982) produced an instruction manual for use with the aerial sampling system; see the original publication for more 

information. 

References

Trial, H., Jr.; Devine, M. E. Spruce budworm in Maine: results of the 1982 project, biological conditions in 1982 and expected infestation conditions for 1983. Tech. Rep. 19. Augusta, ME: Maine Department of Conservation, Maine Forest Service; 1983. 76 p.

McCarthy, J.; Olson, C. E., Jr.; Witter, J. A. 1983. Assessing spruce budworm damage with 

small-format aerial photographs. Canadian Journal of Forest Research 13: 395-399.

Olson, C. E., Jr.; Sacks, P. J.; Witter, J. A.; Bergelin, L. A. 1982. Spruce budworm damage 

assessment with 35 mm air photos: a training manual. Rep. 82-1A. Ann Arbor, MI: The 

University of Michigan, School of Natural Resources, Remote Sensing Laboratory. 41 p.
168

Table 1. Table 1. Hazard-rating system used in Maine during 1982 (modified from Trial and 

Devine 1983).

Current 

defoliation (%) Value

Previous

defoliation (%)1 Value2

Trace 0–5 0 Trace 0–9 0

Light 6–20 1 Light 10–49 3

Moderate 21–50 2 Moderate 50–129 6

Heavy 51–80 4 Severe 130+ 9

Severe 81+ 6

Egg-mass or overwintering larval desposit (number)

Category Egg masses3 Second instar larvae4 Value

Light 0–99 0–175 1

Moderate 100–239 176–500 2

High 240–399 501–1099 3

Very high 400–999 1100+ 5

Extreme 1000+ 1100+ 5

 Total hazard rating 

Tree Vigor Value Category Hazard value

Good (current 

foliage healthy

0 Low 0–6

Fair (shoot production moderate1 Moderate 7–15

Poor (some 

growth capacity)

2 High 16–22

Very poor (nil) 3 Severe 23–26

1

The 2 previous years’ needles.

2

Add three points if there are trees with dead tops in the area (10 to 20 percent of the trees).

3

Number of budworm egg masses/100 ft2 (9.3 m2) of foliage. 4

Number of second-instar budworm larvae/100 ft2 of foliage.
169

Table 2. Stand hazard-rating values obtained from 35-mm photographs and used to predict 

amount potential damage (modified from Olson et al. 1982, McCarthy et al. 1983).

Average stand 

defoliation rank Class label Class value

0.0–1.2 Trace 0

1.3–1.9 Light 1

2.0–2.9 Moderate 2

3.0–4.0 Heavy 3

Stand Mortality (%) Value

Low 0–9 0

Medium 10–29 2

High 30–49 4

Severe ≥50 6

Proportion of stand in host species

Stand density value

Proportion of host species Open Average Dense

<30 1 1 2

30 to 60 2 2 4

>60 3 4 6

Standard hazard-rating value

Category Hazard value

Low 0–4

Moderate 5–8

High 9–10

Severe 11–15
170

Western Spruce Budworm

Choristoneura occidentalis Freeman

Lepidoptera: Tortricidae

Buffam, P. E.; Carolin, Jr., V. M. 1966. Determining trends in western spruce budworm egg 

populations. Journal of Economic Entomology 59: 1442-1444.

Objective

To develop a method of analyzing population trends of C. occidentalis in the Pacific Northwest using egg mass surveys from a single year.

Abstract

Western spruce budworm, Choristoneura occidentalis Freeman, is a widely distributed and 

destructive defoliator of conifers in western North America. Larvae feed on needles, buds, 

staminate flowers, and developing conelets. Repeated defoliation may result in topkill, further reducing cone production for years after outbreaks subside. Young seedlings attacked 

by C. occidentalis often die, reducing stand densities and retarding forest regeneration. 

Periodic outbreaks occur irregularly and can last for decades. Severely defoliated Douglasfir [Pseudotsugae menziesii (Mirb.) Franco] may be susceptible to subsequent attack by 

Douglas-fir beetle, Dendroctonus pseudotsugae Hopkins, and the fir engraver beetle, Scolytus 

ventralis LeConte.

Population trends of C. occidentalis can be predicted when egg mass densities on foliage are 

categorized as either new or old. New egg masses are those deposited during the current year 

while old egg masses are those laid the previous year, or two years ago in some cases. Old 

egg masses represent the new egg masses laid the previous year so that population estimates 

can be made for both the current year and the previous year using the same sample branch 

samples. The difference between the number of new and old egg masses, compared using a 

standard t test, can be used to determine if populations are declining, increasing, or remain 

static when significant at the 5% level of probability. A large number of 1- or 2-year old egg 

masses present in the sample may exaggerate the rate of decline detected by this method. 

However, the technique is still useful in detecting a decline in the population regardless of 

rate. This method was tested using data collected from white fir, Abies concolor (Gord. & 

Glend.) Lindl. ex Hildebr., stands in the Pacific northwestern USA over 4 consecutive years.

Sampling Procedure

Sample egg masses after the oviposition period has ended, generally by late August in the Pacific Northwest. A minimum of 4 plots should be sampled in areas of 6,070–16,187 ha, and 
171

at least 1 plot per 4,047 ha for even larger areas. Each plot should contain five co-dominant 

trees of a single host species.

Sample 2 mid-crown branches from the same or adjacent whorls from each tree within a 

plot for a total of 10 branches per plot. Branches should be taken from opposite sides of the 

tree. Strip and discard the twigs from the left side of the first branch and the right side of 

the second branch. Calculate the area of the foliated area of the two half-branch sample by 

multiplying the measurements of the total length and the width at mid-length on the foliated 

side (Morris 1955). Closely examine each sample for C. occidentalis egg masses. Separate 

all egg masses present into new and old age categories. Characteristically new egg masses 

appear shiny with visible emergence holes if the larvae have eclosed. New eggs with larvae 

that failed to eclose are greenish turning to yellowish. Old egg masses appear milky, have an 

eroded surface, and are brownish if the larvae failed to eclose. Old egg masses may be present on foliage for more than a year. Tally the number of old and new egg masses and compare these numbers using a student’s t test. If the differences between densities of old and 

new egg masses within a given year are significant at the 5% level of probability, a positive 

difference indicates a building population while a negative difference indicates a population 

in decline. Differences close to zero would indicate a static population trend.

Notes

Workers must be able to distinguish between old and new egg masses. Branches should be 

re-examined at least partially by a second worker to check that egg masses are counted and 

categorized by age correctly. The authors calculated the density of egg masses per 1,000 in2 

of foliage, but density data based on branch area are not necessary if examining the population trend within a single sampling season. Egg mass density can be expressed as egg masses 

per m2 if population trends will be compared over multiple years.

This 1966 paper was published before Freeman (1967) separated western spruce budworm, 

Choristoneura occidentalis, from the closely related spruce budworm, Choristoneura 

fumiferana. 

References

Freeman, T. N. 1967. On coniferophagous species of Choristoneura (Lepidoptera: Torticidae) 

in North America. I. Some new forms of Choristoneura allied to C. fumiferana. Canadian 

Entomologist 99: 449-455.

# Morris, R. F. 1955. The development of sampling techniques for forest insect defoliators, 

with particular reference to the spruce budworm. Canadian Journal of Zoology 33: 225-

294.
172

Western Spruce Budworm

Choristoneura occidentalis Freeman

Lepidoptera: Tortricidae

Harris, J. W. E.; Dawson, A. F.; Brown, R. G. 1981. Selecting sampling points for larvae of 

western spruce budworm, Choristoneura occidentalis (Lepidoptera: Tortricidae), from survey records in British Columbia. Journal of the Entomological Society of British Columbia 

78: 7-9. 

Objective

To reduce sampling effort for populations of C. occidentalis by selecting sampling locations 

from survey records.

Abstract

Western spruce budworm, Choristoneura occidentalis Freeman, is a periodically severe defoliating pest of Douglas-fir, Pseudotsuga menziesii (Mirb.) Franco, and true firs (Abies spp.) in 

northwestern North America. The Canadian Forestry Service conducts an annual survey of 

Douglas-firs for C. occidentalis larvae as part of its Forest Insect and Disease Survey (FIDS). 

The authors examined the historical database produced by FIDS and applied a set of criteria 

to the records in an attempt to reduce the number of locations sampled annually without altering the population trend estimated using the entire database. Data from 1949 to 1978 were 

analyzed and 17 sampling locations from the original dataset of 98 sites were identified that 

effectively predicted the population trend of C. occidentalis. Sampling a subset of the sites 

allows for a reduction in sampling effort with concomitant savings in labor and associated 

costs.

Sampling Procedure

Only sample C. occidentalis at sites that meet the following criteria:

1. Site has been sampled for 5 or more years.

2. C. occidentalis larvae were found in at least 30% of the years the site has been sampled.

3. Site had a larval population greater than the average of all locations sampled in at

least 30% of the sample years.

4. Site has had noticeable defoliation within the sampled Universal Transverse Mercator

map grid for 5 or more years. 

5. Defoliation at the site was visible in the first year defoliation was detected within the

surrounding drainage.
173

Sample 3 trees of each species of interest at each selected site by laying a 2.1 by 2.7 m white 

sheet under a tree and beating the branches with a 3.7 m pole for approximately 30 secs (Harris et al. 1972). Identify and count the larvae that fall onto the sheet. Sampling from locations that are easily accessible by roads or waterways, i.e. “roadside sampling”, is convenient, economical, and statistically appropriate.

Reference

# Harris, J. W. E.; Collis, D. G.; Magar, K. M. 1972. Evaluation of the tree-beating method 

for sampling defoliating forest insects. Canadian Entomologist 104: 723-729.
174

Western Spruce Budworm

Choristoneura occidentalis Freeman

Lepidoptera: Tortricidae

Schmid, J. M. 1984. Larval densities, mortality, and sampling at different canopy levels during a western spruce budworm (Lepidoptera: Tortricidae) suppression project. Environmental Entomology 13: 781-786.

Objective

To evaluate four sampling designs for C. occidentalis larvae on Douglas-fir and white fir 

before and after chemical application for suppression.

Abstract

Western spruce budworm, Choristoneura occidentalis Freeman, is an important pest of 

Douglas-fir [Psuedotsugae menziesii (Mirb.) Franco], true firs (Abies spp.), Englemann 

spruce (Picea englemannii Parry ex. Englem.), and larch (Larix occidentalis Nutt.) in the 

western US and Canada. Infestations in mature stands result in growth loss, top kill, and 

occasional tree mortality. Post-spray sampling of C. occidentalis larvae has followed a plan 

adapted from C. occidentalis egg mass surveys, where two mid-crown branches are taken 

from opposite sides from each of three Douglas-firs in a cluster, with 25 clusters of trees 

sampled throughout the spray unit. It was unknown if this plan accurately estimated larval 

mortality in lower crowns. Four new sampling designs were evaluated on Douglas-fir and 

white fir [Abies concolor (Gord. & Glend.] in northern New Mexico, USA. The sampling 

plans consisted of taking from each plot: 1) one branch from a single tree, 2) one branch from 

each tree in a cluster of three trees, 3) two branches from each tree in a cluster of three trees, 

or 4) a single branch from each tree in a cluster of six trees.

Variation in larval densities among crown levels was rarely significant in Douglas-fir, suggesting that samples could be collected from the lower crown if the lowest branches were 

avoided, thereby decreasing sampling effort and costs. In contrast, sampling should be 

restricted to the mid-crown of white fir due to inter-crown variation when sampling from 

clusters of trees. Sampling additional branches from a crown level, or taking one branch 

from both the mid- and lower crown increased sampling effort and cost with no improvement 

in sampling precision.

Mid-crown larval densities were similar for all four plans, indicating that each design was 

similar in accuracy. While the single branch per tree plan was the most economical in terms 

of sampling costs, it was not as precise as the other plans using clusters of trees at a higher 

associated cost of sampling. Significant variation among plots was observed, but significant 
175

variation among trees within plots was minimal. These results suggest that sampling a larger 

number of plots would improve the precision of all designs, but sampling a cluster of three or 

more trees does not necessarily improve the precision while increasing the cost of sampling. 

The authors recommended sampling a single branch from one tree in each plot if reduced 

sampling cost was desirable, or sampling a single branch from each tree in a cluster of three 

trees within a plot if increased precision was more desirable. Either design was considered 

advantageous over the current sampling plan of two mid-crown branches from each of three 

Douglas-firs in a cluster. 

Sampling Procedure

Refer to Table 4 for the appropriate number of plots and trees needed to obtain the desired 

coefficient of variance of 0.1 or 0.2. Establish the appropriate number of plots 100–200 m 

apart in areas to be sampled. Plots must be evenly distributed throughout the sampling area 

to fully measure the variation in C. occidentalis larval populations.

Each plot should contain seven Douglas-fir or white fir trees 8–15 m tall, even though not all 

seven trees will be sampled. Randomly select trees for sampling within each plot. Sample 

either Douglas-fir or white fir; do not combine data from both tree species. If minimizing 

sampling costs is an objective, sample one branch from a single tree within each plot. If 

increased precision is an objective, sample one branch from each tree in a cluster of 3 trees 

within each plot. Sample trees when third and fourth C. occidentalis instars are present. Collect pre- and post-spray samples if studying the efficacy of chemical suppression. 

Using pole pruners, sample 35–50 cm branch tips from either the middle crown of white fir 

or from the lower crown of Douglas-fir. Avoid sampling the lowest branches on Douglasfir. Record the number of C. occidentalis larvae, the number of new shoots or buds present, 

and the overall area of the branch (cm2

) for each sample. Express the data as the number of 

larvae per 100 buds and/or m2 of foliage.
176

Table 4. Number of plots and trees needed to obtain coefficients of variation (CV) of 0.1 and 

0.2 of the mean number of larvae per m2 of foliage on middle crown branches of Douglas-fir 

and white fir.

Host Sampling Design

 Sampling 

period

CV

1 branch 

from 1 tree

1 branch from 

each of 3 trees

2 branches from 

each of 3 trees

1 branch from 

each of 6 trees

Douglas fir

Before spraying

0.1 76 (76)a 40 (120) 35 (105) 30 (180)

0.2 19 (19) 10 (30) 9 (27) 8 (48)

After spraying

0.1 272 (272) 175(525) 113 (339) 154 (924)

0.2 68 (68) 44 (132) 28 (84) 39 (234)

White fir

Before spraying

0.1 48 (48) 20 (60) 20 (60) 14 (84)

0.2 12 (12) 5 (15) 5 (15) 3 (18)

After spraying

0.1 320 (320) 174 (522) 107 (321) 133 (798)

0.2 80 (80) 43 (129) 27 (81) 33 (198) a Number of trees in parenthesis.

Table 4 reproduced with permission of Environmental Entomology, granted April 2, 

2009.
177

Western Spruce Budworm

Choristoneura occidentalis Freeman

Lepidoptera: Tortricidae

Schmid, J. M.; Farrar, P. A. 1982. Distribution of western spruce budworm egg masses on 

white fir and Douglas-fir. Res. Pap. RM-241. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station; 7 p.

Objective

To improve sampling precision by determining the inter- and intra-crown distribution of C. 

occidentalis egg masses on white fir and Douglas-fir.

Abstract

Western spruce budworm, C. occidentalis Freeman, is a periodically severe defoliating pest 

of Douglas-fir, Pseudotsuga menziesii (Mirb.) Franco, and true firs (Abies spp.) in northwestern North America. Egg sampling is preferred over the other life stages of this pest due to 

ease of sample collection and assessment. Moreover, egg density of C. occidentalis can be 

sampled long in advance of damage, which helps when planning suppression projects for this 

pest.

Variation in egg mass densities was studied between and within trees to improve sampling 

precision. White fir was included in the study as the distribution of C. occidentalis on this 

host is not well known but is of importance when white fir occurs in mixed stands with 

Douglas-fir. Inter-tree variation in egg mass counts was high for both white fir and Douglasfir. Consequently, the number of trees selected for sampling should be large enough to account for this variation. Earlier studies have suggested that 25 trees within the area of concern should be sufficient (McKnight et al. 1970). Egg mass density did not vary significantly 

between the lower and middle crown of Douglas-fir. Moreover, egg mass density did not 

vary significantly among cardinal directions sampled. 

Sampling Procedure

Randomly select 25 trees within an area of concern. Randomly cut one 45-cm long (18 in.) 

branch from either the lower or midcrown of each tree and examine for egg masses. Avoid 

lower branches with little or no foliage. Trees should be widely spaced throughout the stand 

to account for variability within the stand. If the stand is homogeneous, it may be possible to 

reduce sampling effort somewhat by sampling 9 clusters of 3 trees each, spaced throughout 

the stand. 
178

Note

This sample methodology should yield more precise estimates of budworm density for management decision-making, but no decision level was established for egg mass densities.

Reference

* McKnight, M. E.; Chansler, J. F.; Cahill, D. B.; Flake, H. W., Jr. 1970. Sequential plan for 

western budworm egg mass surveys in the central and southern Rocky Mountains. Res. 

Note RM-174. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Forest and Range Experiment Station; 8 p.
179

Western Spruce Budworm

Choristoneura occidentalis Freeman

Lepidoptera: Tortricidae

Shore, T. L.; Alfaro, R. I. 1988. Predicting Douglas-fir defoliation from the percentage of 

buds infested by the western spruce budworm. Journal of the Entomological Society of 

British Columbia 85: 21-25.

Objective

To predict stand defoliation by C. occidentalis on Douglas-fir using logarithmic or linear 

regression models based on the percentage of infested flower and foliage buds.

Abstract

Western spruce budworm, Choristoneura occidentalis Freeman, is an important pest of 

Douglas-fir [Psuedotsugae menziesii (Mirb.) Franco], true firs (Abies spp.), Englemann 

spruce (Picea englemannii Parry ex. Englem.), and larch (Larix occidentalis Nutt.) in the 

western US and Canada. Infestations in mature stands result in growth loss, top kill, and occasional tree mortality.

The authors explored the relationship between the percentage of Douglas-fir buds infested by 

C. occidentalis and the resulting stand defoliation observed that summer. Buds from the midcrown of three dominant or co-dominant trees were sampled for infestation and percentage 

infestation was calculated as an average of the three tree estimates. A total of 60 combined 

estimates of percentage bud infestation were made between 1977 and 1982 from 12 locations. When larval feeding had ended in late summer of the same year, percent defoliation 

was estimated from 10 randomly selected dominant or co-dominant trees at the same locations using binoculars. Defoliation estimates were made from the upper-, mid-, and lowercrowns of each tree. Total tree defoliation was calculated from the crown-level estimates of 

each tree and stand defoliation was calculated as the average of the 10 tree estimates.

A statistically significant relationship exists between the percentage of buds infested by C. 

occidentalis and the percent stand defoliation. The best regression model for this relationship 

is a logarithmic model:

ln (Defoliation + 1) = –0.3491 + 1.2053 ln(Buds + 1)

where ln = natural logarithm, Defoliation = average tree defoliation (%) per location and 

Buds = % buds infested by WSB per location (R2

 = 0.76, F = 136.8, MSE = 0.667, P < 0.01, 

and n = 46). A simple linear model is also appropriate:
180

Defoliation = –0.3491 + 1.2053(Buds)

where Defoliation = average tree defoliation (%) per location and Buds = % buds infested by 

WSB per location (R2

 = 0.68, F = 94.4, MSE = 0.319, P < 0.01, and n = 46). The linear model is easy to use and closely fits the model developed by Carolin and Coulter (1972), therefore the authors recommend its use even though the logarithmic model was more precise.

Sampling Procedure

It is critical that Douglas-fir buds are sampled at the appropriate phenological stage, between 

the white scale stage and the brush stage as defined by Shepherd (1983, see publication for 

photographs of each stage of bud development). Larvae should be third and fourth instars 

during these stages.

Using pole pruners, sample mid-crown branch tips until 100 buds have been examined from 

each of three dominant or co-dominant trees (i.e. , a total of 300 buds). Calculate the percentage of infested buds as an average of the three individual tree estimates. Use Table 1 to 

determine the expected defoliation associated with the percentage of infested buds as predicted by either the logarithmic or linear model. Defoliation classes are those used by the 

Canadian Forestry Service in British Columbia. 

Notes

The model will overestimate stand defoliation if high larval mortality occurs after infested 

buds have been sampled. Sampling infested buds allows managers to define areas where 

sprays are not likely to be needed after winter mortality and check the effectiveness of earlier spray applications. However, sampling infested buds will not allow operation managers 

enough time to plan and execute spray operations to avoid defoliation by the current generation of larvae. Spray operations for current outbreaks of C. occidentalis larvae should be 

based on egg mass surveys or other sampling methods conducted in the fall.

References

* Carolin, V. M.; W. K. Coulter. 1972. Sampling populations of western spruce budworm and 

predicting defoliation on Douglas-fir in eastern Oregon. USDA For. Serv. Res. Pap. PNW149; 38 pp.

Shepherd, R. F. 1983. A technique to study phenological interactions between Douglas-fir 

buds and emerging second instar western spruce budworm. In R. L. Talerico and M. Montgomery (Eds.). Forest defoliator-host interactions: a comparison between gypsy moth and 

spruce budworms. Conference Proceedings. USDA For. Serv. Gen. Tech. Rpt. NE-85; 141 

pp.
181

Table 1. Percentage of buds infested by western spruce budworm and expected defoliation of 

Douglas-fir.

Buds infested (%) Expected defoliation

Linear Logarithmic Class Percent

0 0 none 0

1–13 1–19 lihgt 1–25

14–35 20–41 moderate 26–65

36–100 42–100 severe 66–100

Table 1 reproduced with permission from the authors, granted April 7, 2009.
182

Western Spruce Budworm

Choristoneura occidentalis Freeman

Lepidoptera: Tortricidae

Srivastava, N.; Beckwith, R. C.; Campbell, R. W.; Torgersen, T. R. 1981. A method for 

sampling western spruce budworm pupae. Res. Note PNW-372. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Forest and Range Experiment 

Station; 6 p.

Objective

To validate a method of stratified sampling for C. occidentalis pupae on branch tips.

Abstract

Western spruce budworm, Choristoneura occidentalis Freeman, is an important pest of 

Douglas-fir [Psuedotsugae menziesii (Mirb.) Franco], true firs (Abies spp.), Englemann 

spruce (Picea englemannii Parry ex. Englem.), and larch (Larix occidentalis Nutt.) in the 

western US and Canada. Infestations in mature stands result in growth loss, top kill, and occasional tree mortality.

Methods of sampling spruce budworm, Choristoneura fumiferana (Clemens), were validated 

for use with C. occidentalis pupae in north-central Washington. Mean pupal densities from 

45-cm branch tips accurately estimated the whole-tree pupal density. Pupal densities varied 

among the upper, middle, and lower crown levels, thus stratified sampling from all three 

levels is recommended. Pupal densities did not differ among terminal tips, lateral tips, and 

whole branches. The recommend sampling unit consists of two 45-cm terminal or lateral 

branch tips from each crown level of a tree. In this study, the mean number of pupae per 

100 buds ranged 0.6–11.3 (± 0.18–3.50 SEM). At this range of densities, the authors suggest 

sampling 10 clusters of three trees each to estimate pupal densities with a 15% level of precision.

Sampling Procedure

Conduct sampling in early to mid-July when most C. occidentalis larvae have pupated. Randomly select 10 sampling points within 5-ha plots. At each point, randomly select three host 

trees for sampling. Divide the live crown of each tree into thirds. Cut at least two 45-cm 

terminal or lateral branch tips from the upper, middle, and lower crown levels of each tree. 

Count the number of pupae and the number of buds present on each branch tip. Express pupal densities as counts per m2 foliage or per 100 buds. 
183

Check the precision level after sampling 10 clusters of three trees each. In this study, the 

mean number of pupae per 100 buds ranged 0.6–11.3 (± 0.18–3.50 SEM). At this range of 

densities, the authors suggest sampling 10 clusters of three trees each to estimate pupal densities with a 15% level of precision. Continue sampling if the desired precision level has not 

been met yet. 

Note

The authors found that variation among trees had a greater effect on pupal density than variation among clusters, therefore they suggest sampling additional trees in each cluster rather 

than including additional clusters of three trees each.
184

Western Spruce Budworm

Choristoneura occidentalis Freeman

Lepidoptera: Tortricidae

Torgersen, T. R.; Scott, D. W.; Gillespie, A. J. R.; Hosman, K. P. 1994. Relationship between 

lower-crown sampling and midcrown sampling for Choristoneura occidentalis (Lepidoptera: Tortricidae) after treatment with Bacillus thuringiensis. Journal of Economic 

Entomology 87: 1022-1026.

Objective

To develop a model predicting mid-crown densities of late-instar C. occidentalis based on 

lower-crown densities.

Abstract

Western spruce budworm, C. occidentalis Freeman, is an important defoliator of Douglas-fir, 

Pseudotsuga menziesii (Mirb.) Franco, and true firs (Abies spp.) in western North America. 

Infestations in mature stands cause growth loss, top kill, and occasionally tree mortality. 

Traditional sampling methods for C. occidentalis include sampling midcrown foliage using 

pole-pruners and beating lower crown foliage over a beatsheet. The beatsheet technique is 

quicker and less expensive than the use of pole-pruners. 

Mid- and lower crown larval densities of C. occidentalis were studied on grand-fir, Abies 

grandis (Dougl. ex D. Don) Lindl., and Douglas-fir in Oregon. An equation was developed 

to estimate the density of late instars found in the midcrown based on larval densities found 

in the lower crown by beatsheeting. The developed equation is helpful in relating the simpler, quicker lower crown sampling to the more time-consuming mid-crown sampling.

Sampling Procedure

Sample when late instars (fourth to sixth instar) are present. Select 25 plots to sample, each 

containing a cluster of five Douglas-fir or grand fir trees. Trees should be 6–14 m tall. In 

each plot, sample three lower-crown branches from each of the five trees in the cluster by 

striking the branch over a beatsheet. Count all C. occidentalis larvae in the fourth, fifth, and 

sixth instar. 

Use the following equation to predict the number of fourth to sixth instar per midcrown tip 

from the number of larvae per 3-tip lower-crown sample in 25 tree clusters of five trees each:

Y = 0, for X < 0.9764
185

Y = –1.0168 + 1.0414X, for 0.9764 < X < 22.0

where Y = the estimated mean number of late instar larvae per midcrown tip and X = the 

mean number of larvae per 3-tip lower-crown sample (R2

 = 0.932, MSE= 4.5189). 

Notes

The authors do not recommend extrapolating above 22 C. occidentalis larvae per sample as 

that was the upper limit of their data. In addition, their model is based on sampling 25 plots 

with 5 trees per plot (n = 125 trees). The model is less accurate in predicting the number of 

late instar C. occidentalis per branch tip in the mid-crown if fewer plots or fewer trees per 

plot are used. The model should not be used to predict midcrown densities in single trees 

because of inter-tree variation.
186

Jack Pine Budworm

Choristoneura pinus pinus Freeman

Lepidoptera: Tortricidae

Hall, R. J.; Volney, W. J. A.; Wang, Y. 1998. Using a geographic information system (GIS) to 

associate forest stand characteristics with top kill due to defoliation by the jack pine budworm. Canadian Journal of Forest Research 28: 1317-1327. 

Objective

To relate stand characteristics with top kill by C. pinus pinus using a geographic information 

system (GIS).

Abstract

The jack pine budworm, Choristoneura pinus pinus Freeman, is a native, periodically significant defoliator of jack pine, Pinus banksiana Lamb., in North America. Top kill is common during outbreaks, but tree mortality is rare unless infestations coincide with periods of 

drought.

Using 1:900 and 1:2500 large-scale color photographs from a forested area in Saskatchewan, 

Canada, top kill by C. pinus pinus was categorized into discrete levels of severity according to a classification system devised by the authors (Table 1). Forest inventory maps were 

converted in GIS for spatial analysis of stand characteristics of age, height, crown closure, 

and site quality. Maps of top kill and stand characteristics were analyzed spatially to identify 

stands vulnerable to damage by C. pinus pinus. Overall, site quality was strongly associated 

with top kill by C. pinus pinus (x2

 = 350.4; α = 0.05), followed in ranking by stand maturity 

(x2 = 328.9; α = 0.05) and stand height (x2

 = 294.3; α = 0.05). Stands associated with moderate and severe top kill were overmature (>85 years), 15–20 m tall, with 31–55% crown closure and established on sites with poor quality. Land managers should consider treatment 

or harvest options for stands with these characteristics to avoid or limit top kill by C. pinus 

pinus.

Sampling Procedure

Using a GIS, convert 1:12,500 scale forest inventory maps of the area under study from vector to raster with a cell size of 12.5 m. Classify forest cover polygons separately according 

to stand age, stand height, and crown closure. Stand age classes can be condensed to young 

(25–35 years), mature (45–65 years), or overmature (85–125 years). Assign site quality 

by interpreting landform and plant community patterns from 1:12,500 and 1:5,000 infrared aerial photographs of the area. Randomly establish field plots of approximately 300 m2
187

within the area being mapped. At each field plot, record data on the plot location, soil profile, 

vegetative community, drainage, slope, aspect, and other related measurements. Produce a 

map of site quality using this field information and 1:25,000 large-scale color photographs. 

Map the distribution of stand maturity, height, and crown closure as a ratio of the area relative to the entire study area.

Stands identified as >85 years old, 15–20 m tall, with 31–55% crown closure and established 

on sites with poor quality should be harvested to avoid or limit top kill by C. pinus pinus. 

Stands with these structural characteristics were associated with moderate and severe ratings 

of top kill (Table 1).

Notes

The authors established 27 field plots within the 44-km2

 study area. Refer to the original 

publication for more details regarding mapping stand characteristics within GIS.

Table 1. Top-kill map classification system.

Severity rating Class limits (%) Description

Nil 0 No visible top kill

Light 1–25 Up to 25% of trees in delineated polygons with visible 

top kill

Moderate 26–50 From 26 to 50% of trees in delineated polygons with 

visible top kill

Severe 51–100 > 50% of trees in delineated polygons with visible top 

kill

Unclassified Not applicable Regenerating areas or areas not supporting forest 

stands

Table I reprinted with permission from NRC Research Press, granted June 8, 2009. (© 

2008 NRC Canada or its licensors. Reproduced with permission.)
188

Jack Pine Budworm

Choristoneura pinus pinus Freeman

Lepidoptera: Tortricidae

Nealis, V. G.; Lomic, P. V. 1996. Forecasting defoliation by the jack pine budworm. Frontline Tech. Note No. 91. Sault Ste. Marie, Ont.: Natural Resources Canada, Canadian Forest 

Service; 4 p.

Objective

To describe an improved method of forecasting defoliation by C. pinus pinus in individual 

stands.

Abstract

Jack pine budworm, Choristoneura pinus pinus Freeman, is a native, periodically significant 

defoliator of jack pine, Pinus banksiana Lamb., in North America. Prior to this effort, defoliation assessments for jack pine budworm had been adapted from those used for the spruce 

budworm, Choristoneura fumiferana (Clemens). However, surveys that related egg and/or 

overwintering larvae densities to subsequent defoliation by spruce budworm were inaccurate 

for predictions of jack pine budworm defoliation. This method was improved by including 

estimates of jack pine pollen cone abundance, an important food source for the survival of 

young jack pine budworm larvae.

A prediction model was developed whereby estimates of egg mass and/or pollen cone density 

are used to predict defoliation level. The model was tested against 35 independent data sets 

and was found to be 77% accurate if using the lowest defoliation class (30% needles consumed) and 89% accurate at the highest defoliation class (>75% needles consumed). This 

level of accuracy was deemed acceptable for forecasting of budworm defoliation by monitoring and/or for suppression projects. Egg mass sampling can also be combined with defoliation and/or pollen cone surveys to save time and handling of foliage.

Sampling Procedure

Sample egg masses in August and September. Pollen cones can be sampled from October 

through April, but sampling in fall or winter provides more time to implement control tactics 

than sampling in the spring.

To sample either egg masses or pollen cones, randomly sample a 1-m terminal branch from 

the midcrown of six randomly chosen jack pines in a stand. Count the total number of egg 

masses and pollen cones (microstrobili) on the branch samples. Log (log10) transform each 
189

estimate, then use them in the following equation to estimate the density of feeding jack pine 

budworm larvae per 100 shoots (Nt+1):

 

Nt +1 = 10(1.31*EGG + 0.70)

10(−.60*CONE + 0.94)

where Nt+1 is the estimated number of feeding larvae per 100 shoots, EGG is the log10

density of egg masses per 1-m branch sample, and CONE is the log10 density of pollen cones 

per 1-m branch sample.

Compare the density estimate to the following density thresholds that coincide with estimated 

levels of defoliation.

Budworm density per 100 shoots Defoliation

>6.1 >30%

>16.8 >50%

>55.2 >75%

Defoliation can be assessed from the same 1-m branches sampled for egg masses and/or 

pollen cone assessments. Defoliation is usually estimated from one 1-m branch sample taken 

from 6-10 trees.

Defoliation Percentage current-year shoots removed

light <25%

moderate 25–75%

severe >75%

Notes

Obviously sampling for feeding budworm larvae provides a reasonably accurate estimate of 

defoliation potential (see Fig. 2 in original publication; r2 = 0.62). However, sampling for 

feeding larvae is time consuming and tedious. Moreover, larval samples must be made in 

late spring, which does not give managers sufficient lead-time to implement control tactics.
190

Jack Pine Budworm

Choristoneura pinus pinus Freeman

Lepidoptera: Tortricidae

Volney, W. J. A. 1992. The distribution and estimation of jack pine budworm defoliation. 

Canadian Journal of Forest Research 22: 1079-1088.

Objective

To streamline estimation of defoliation by C. pinus pinus on current year shoots and to estimate defoliation levels at a level of fixed precision.

Abstract

Jack pine budworm, Choristoneura pinus pinus Freeman, is an important defoliator of jack 

pine, Pinus banksiana Lamb., and to a lesser extent on red pine, P. resinosa Ait., in the Great 

Lakes region and Canada. Extensive top kill is common during outbreaks, but tree mortality 

is rare unless infestations coincide with periods of drought.

Defoliation by C. pinus pinus varies among stands, among trees, and within tree crowns. 

This variation was quantified and used to determine sample sizes needed to estimate defoliation levels with fixed levels of precision. Ten shoots from a 45-cm branch are used to estimate defoliation levels. The number of plots, trees, and branches needed to estimate C. pinus 

defoliation levels to within 5% of the true mean varied with the mean of the true defoliation 

levels. In general, the least amount of total effort was needed if true defoliation levels were 

<10 and >80%. Depending on the defoliation level in the stand, between 3 to 6 h are needed 

to complete the survey.

Sampling Procedure

Refer to Table 7 for the appropriate number of plots, trees, and branches to sample. Randomly select trees within plots and branches on trees, although systematic sampling of trees 

along a transect may be more appropriate than random selection given the variation in defoliation that occurs within a stand. Each sampled branch should be at least 45-cm in length. 

Branches sampled from the middle third of the live crown produce defoliation estimates with 

the smallest sampling bias. Estimate the defoliation level of 10 randomly selected shoots on 

each branch following the method of Fettes (1950). The Fettes method estimates defoliation 

of shoots in 10% defoliation level classes. Average the estimates of defoliation level among 

trees within a stand to determine stand defoliation levels within 5% of the true mean.
191

Notes

The 5% level is very precise and might not be efficient for surveys of large areas for management purposes. This procedure could be used to estimate C. pinus pinus density (see Nealis 

and Lomic 1996, this volume).

References

* Fettes, J. J. 1950. Investigations of sampling techniques for population studies of the spruce 

budworm on balsam fir in Ontario. Annual Rep. 1949. Sault Ste. Marie: Canadian Forest 

Service, Forest Insect Laboratory; 11 p.

# Nealis, V. G.; Lomic, P. V. 1996. Forecasting defoliation by the jack pine budworm. Frontline 

Tech. Note 91. Sault Ste. Marie: Natural Resources Canada, Canadian Forest Service; 4 p.

Table VII. Optimal allocation of effort in a sample scheme to estimate stand defoliation with a 

standard error of 5% of the mean.

Percent

defoliation

Number of sample units* Time†

(min) Plots Trees Branches

10 9 1 2 185

20 10 1 2 215

25 9 2 1 217

30 8 2 1 236

40 4 6 1 302

50 3 9 1 363

60 3 9 1 370

70 3 8 1 343

80 3 5 1 275

* Number of units was rounded to the next highest integer.

†

 Estimated time required to complete assessment.

Table VII reprinted with permission from NRC Research Press, granted June 8, 2009. (© 2008 

NRC Canada or its licensors. Reproduced with permission.)
192

Oak Leaftier

Croesia semipurpurana (Kearfott) Freeman

Lepidoptera: Tortricidae

Ciesla, W. M. 1969. Forecasting population trends of an oak leaf tier, Croesia semipurpurana. Journal of Economic Entomology 62: 1054-1056. 

Objective

To describe a fixed sampling method for predicting defoliation by C. semipurpurana based 

on egg density.

Abstract

Oak leaftier, Croesia semipurpurana (Kearfott), is a native defoliator of oaks (Quercus spp.,) 

in the eastern USA. Larvae favor oaks in the red oak group. Young larvae mine oak buds as 

they open in the spring and can remove much of the new growth. Older larvae tie remaining 

leaves together with silk to form feeding refuges. Repeated defoliation over several consecutive years may result in twig and branch dieback, growth loss, and susceptibility to attack by 

secondary insects and fungi.

Eggs are present from midsummer through the following spring, therefore egg density can 

be sampled in winter and used to forecast population trends. The distribution of C. semipurpurana eggs on branches of scarlet oak, Quercus coccinea Muenchh., was studied to develop 

a fixed sampling method to predict subsequent defoliation by this pest. Mean C. semipurpurana egg density on a 0.38 m terminal branch sample is positively related (r = 0.79) to the 

percentage of defoliation. This relationship can be expressed by the following equation:

y = 2.18 + 2.56x

where x = the mean number of eggs per 0.38 m terminal branch sample and y = percentage 

of expected defoliation. Land managers can use this formula to decide if C. semipurpurana

populations warrant control measures.

Sampling Procedure

In each designated plot, randomly select 4 scarlet oaks or other members of the red oak 

group in late winter. Using pole pruners, randomly cut two 0.38 m terminal branches from 

the lower or mid-crown of each tree. Discard any lateral branches or previous years’ growth 

from the samples. Bag samples and transport to a laboratory. Using a stereomicroscope, examine each sample for C. semipurpurana eggs, paying close attention to the upper surface of 
193

the branch, and areas of rough bark or leaf scars. A larger percentage of eggs will be found 

on the basal section of the branch sample. Use the formula y = 2.18 + 2.56x, where x = the 

mean number of eggs per 0.38 m terminal branch sample and y = percentage of defoliation, 

to predict the level of expected defoliation. Alternatively, consult Table 2 to predict the level 

of expected defoliation.

 

Note

The sampling plan was developed on scarlet oaks in the mid-Atlantic. Results may vary on 

different host trees in other regions.

Table 2. Mean number C. semipurpurana eggs per 15-in. branch sample and predicted defoliation, James River District, George Washington National Forest, Va. 1967.

Mean no. eggs/15 

in. sample Predicted % Defoliation

0–8.0 Negligible-light 0–25

8.1–28.2 Moderate 26–75

>28.2 Heavy ≥76

Table 2 reproduced with permission from the Journal of Economic Entomology, granted April 2, 2009.
194

Spruce Bud Moth

Zeiraphera canadensis Mutuura & Freeman

Lepidoptera: Tortricidae

Régnière, J.; Boulet, B.; Turgeon, J. J. 1988. Sequential sampling plan with two critical levels 

for spruce bud moth (Lepidoptera: Tortricidae). Journal of Economic Entomology 81: 220-

224.

Objective

To develop a sequential sampling plan for Z. canadensis in white spruce plantations based on 

larval density.

Abstract

Spruce bud moth, Zeiraphera canadensis Mutuura & Freeman, is a pest of white spruce, 

Picea glauca (Moench) Voss in eastern North America. Larvae tend to feed on the current 

year’s growth in the upper crown, limiting damage to reduced growth and crown distortion. 

Damage is particularly important in plantations of white spruce, where reduced growth may 

postpone harvest for several years. Older larvae occasionally feed on the bark of tender 

twigs, which may predispose the tree to attack by wood boring insects and decay fungi. In 

the urban setting, unsightly feeding damage may be of a concern to homeowners.

The authors developed a sequential sampling plan with a 68% confidence level for Z. 

canadensis larvae based on two critical levels of two and eight larvae per sample. 

Larvae per branch sample Threshold density

μ ≤ 2 Low

2<μ ≤ 8 Moderate

μ > 8 High

Previous research used a critical level of 5 larvae per branch sample (Turgeon and Régnière 

1987). Generally 20 branch samples were needed for larval densities near the lower critical 

level, but smaller sample sizes were required for most densities. Typically the plan took 1–2 

hours to conduct in plantations where more than 1.5 larvae were found in each sample. At 

least 16 min were needed for a plantation when no spruce bud moth larvae are found on the 

minimum sample size of only five branches. Because larvae aggregate in the crown, excessively large sample sizes would be needed for confidence levels of 90–95%, which would 

make the plan impractical for most pest managers.
195

Sampling Procedure

Select white spruce trees at equally spaced intervals throughout the plantation. Randomly cut 

one 15-cm terminal branch from the upper third of the crown of each tree.

Count the number of third and fourth instars in each 15-cm branch sample as a cumulative 

tally. Compare the cumulative tally to Table 1. Counts can be classified as low, moderate, or 

high if the cumulative tally crosses the boundaries of the lower or upper critical zones. If the 

tally remains in a zone, continue sampling until 50 branch samples have been examined in 

the lower critical zone or 20 branch samples have been examined for the upper critical zone. 

If a maximum of 50 branch samples are examined in the lower critical zone, the population 

can be classified as low if n <100 larvae or as moderate if n ≥100 larvae. If a maximum of 

20 branch samples are examined in the upper critical zone, the population can be classified as 

moderate if n <160 larvae or as high if n ≥160 larvae.

Notes

Because Z. canadensis larvae tend to occur in aggregations, the authors caution that trees 

should be selected for sampling at random throughout the plantation to avoid biasing the 

counts and misclassifying the infestation. This sampling plan was developed for use in white 

spruce plantations in northern New Brunswick and Québec. The authors do not recommend 

its use in natural stands or plantations outside of these regions without further testing.

Reference

* Turgeon, J. J.; Régnière, J. 1987. Development of sampling techniques for the spruce budmoth, Zeiraphera canadensis (Lepidoptera: Tortricidae). Canadian Entomologist 119: 

239-249.
196

Table 1. Sequential sampling plan with two critical levels for the spruce bud moth in white 

spruce plantations

No. samples

examined

Cumulative no. larvae found (n)a

Lower critical zone Upper critical zone

5

L

O

W

2–18

M

O

D

E

R

A

T

E

20–60

H

I

G

H

6 3–21 27–69

7 4–24 33–79

8 6–26 39–89

9 7–29 46–98

10 9–31 52–108

11 10–34 59–117

12 11–37 66–126

13 13–39 72–136

14 14–42 79–145

15 16–44 86–154

16 18–46 93–163

17 19–49 101–172

18 21–51 107–181

19 22–54 114–190b

20 24–56 Terminal decision:

if n <160, moderate: if n ≥160, high

21 25–59

22 27–61

23 29–63

24 30–66

25 32–68

26 34–70

27 35–73

28 37–75

29 39–77

30 40–80

31 42–82

32 44–84

33 45–87

34 47–89

35 49–91

36 50–94

37 52–96

38 54–98

39 55–101

40 57–103

41 59–105
197

42 61–107

43 62–110

44 64–112

45 66–114

46 67–117

47 69–119

48 71–121

49 73–123

50 Terminal decision:

if n <100, low; 

if n≥100, 

moderate

a

Sampling is pursued until the cumulative number of larvae exits the indicated inclusive ranges, or 

until maximum sample size is reached.

Table 1 reproduced with permission from the Journal of Economic Entomology, granted April 2, 2009.
198

Introduced Basswood Thrips

Thrips calcaratus Uzel

Thysanoptera: Thripidae

Werner, S. M.; Nordheim, E. V.; Raffa, K. F. 2004. Comparison of methods for sampling 

Thysanoptera on basswood (Tilia americana L.) trees in mixed northern hardwood 

deciduous forests. Forest Ecology and Management 201: 327-334.

Objective

To compare three methods of sampling T. calcaratus on basswood.

Abstract

Canopy insects can be sampled using a variety of techniques. Pole pruners are commonly 

used, but foliage can also be collected using a shotgun or tree climbers. These methods were 

used to sample the introduced basswood thrips, Thrips calcaratus Uzel, on American basswood, Tilia americana L. T. calcaratus is associated with defoliation and crown dieback on 

basswood in northern US forests (Raffa and Hall 1989). In general, research objectives, legal 

ramifications, economic costs, and logistics should all be considered when selecting a sampling method for foliage insects.

Each collection method (pole pruner, shotgun, and tree climber) had its advantages and disadvantages. A tree climber who bagged foliage samples before releasing them to the ground 

collected more thrips per sample numerically than the other methods, but bagging the samples was time-consuming and increased the per-hour cost of hiring a certified tree climber. 

Furthermore, the mean density of thrips per sample obtained by a tree climber who did not 

bag the samples before releasing them to the ground did not differ from the mean density of 

thrips obtained by using a pole pruner or a shotgun. The pole pruner or shotgun methods can 

sample multiple areas of the canopy in a shorter period of time compared to a tree climber, 

but pole pruners may not reach all sections of the canopy equally well and the use of a shotgun may not be permitted in certain areas. A tree climber may introduce bias in the selection 

of foliage samples, but it may be comparable to that introduced by someone using a pole 

pruner. 

Samples of T. calcaratus collected by the tree climber were positively related to those collected using pole pruners for both adult (r2

 = 0.73; P = 0.01) and larval thrips (r2 = 0.71; P = 

0.01), whereas samples collected using a shotgun were not related to samples collected by 

either a tree climber or a pole pruner. Densities of adult T. calcaratus obtained by the pole 

pruner method can be used to predict densities obtained by the tree climber using the formula 

y = 0.83x + 0.04, where y = density of thrips obtained by pole pruner method and x = density 

of thrips obtained by tree climber method.
199

Sampling Procedure

The sample unit consists of the three terminal leaves or leaf buds on each small basswood 

branch. Bag each sample separately for transportation back to the laboratory. Place each 

sample in 70% ethanol and collect the thrips present onto filter paper using vacuum filtration. 

Identify the species and life stage of the thrips under magnification.

Collect foliage samples from basswood trees using one of the following techniques:

1. Use a 1.83 m pole pruner to cut small branches from the canopy. Use 1.83 m 

pole extensions as needed.

2. Use a 20-gauge (15.90 mm bore diameter) shotgun to blast small branches 

 from the canopy.

3. Hire a certified tree climber to climb trees, clip small branches, and either 

 drop the branches to the ground or bag each branch separately before dropping 

 them to the ground.

Thrips densities obtained by the tree climber or pole pruner methods are highly correlated 

and one can be used to predict the results of the other method. Use the following equation:

y = 0.83x + 0.04

where y = density of adult T. calcaratus obtained by pole pruner method and x = density of 

adult T. calcaratus obtained by tree climber method.

For T. calcaratus larvae, use the following equation:

y = 0.56x + 0.74

where y = density of larval thrips obtained by pole pruner method and x = density of larval 

thrips obtained by tree climber method.

Notes

Eight basswood trees were sampled using each method, but the authors did not specify the 

number of samples taken from each tree. Contact the authors for additional information.

Reference

Raffa, K. F.; Hall, D. J. 1989. Thrips calcaratus Uzel (Thysanoptera: Thripidae), a new pest 

of basswood trees in the Lake States. Canadian Journal of Forest Research 18: 1661-

1662.
200
201

miscellaneous INSECTS
202
203

Miscellaneous Insects

Mussey, G. J.; Potter, D. A. 1997. Phenological correlations between flowering plants and 

activity of urban landscape pests in Kentucky. Journal of Economic Entomology 90: 1615-

1627.

Objective

To predict pest activity based on the phenology of indicator plants.

Abstract

Pest management decisions are often based on calendar dates because frequent monitoring 

and sampling, which are more effective means of gauging the need for control measures, can 

be time consuming and labor intensive. A phenology calendar that correlates pest activity to 

the flowering of common indicator plants may be more useful for landscape managers with 

little knowledge of pest life cycles or appropriate sampling methodology. Such calendars allow managers to predict when insect pests are likely to be in a life stage vulnerable to control 

measures. Additional scouting can confirm the presence of vulnerable life stages of a given 

pest and when most of the population has entered this stage.

Sampling Procedure

Use Table 4 to identify phenological events of common plants associated with the presence 

of selected insect pests of forest and shade trees. Consider applying control measures during the given phenological events, but after most of the pest population has entered the life 

stage vulnerable to control. Use Table 6 to compare the average dates of phenological events 

between Louisville, Kentucky and Midland, Michigan (Herms 1990).

Note

The phenology calendar was developed in central Kentucky and may be appropriate for surrounding states, but should be used with caution in other regions.

Reference

Herms, D. A. 1990. Biological clocks. American Nurseryman 172: 56-63.
204

Table 4. Phenological sequence of woody plant flowering and insect events in Lexington, 

KY, 1992-1994

Insects Indicator plant Phenological 

event

3-yr. Avg

Acer saccharinum L. 1st bloom 18 Feb.

Cornus mas L. 1st bloom 21 Feb.

Acer saccharinum L. 50% bloom 24 Feb.

Cornus mas L. 50% bloom 03 Mar.

Acer saccharinum L. 95% bloom 10 Mar.

Forsythia xintermedia Zabel 1st bloom 12 Mar.

Cornus mas L. 95% bloom 13 Mar.

Eastern tent caterpillar

Malacosoma americanum (F.) Egg hatch 16 Mar

Berberis x mentorensis L. M. Ames 50% bloom 09 Apr.

Amelanchier arborea (Michaux, F.) 

Fernald 50% bloom

09 Apr.

Viburnum x juddii Rehder 1st bloom 10 Apr.

Magnolia x soulangiana SoulangeBodin 95% bloom

11 Apr.

Acer platanoides L. 50% bloom 11 Apr.

Amelanchier arborea (Michaux, F.) 

Fernald 95% bloom 11 Apr.

Malus floribunda Van Houtte 1st bloom 11 Apr.

Berberis x mentorensis L. M. Ames 95% bloom 13 Apr.

Viburnum x juddii Rehder 50% bloom 13 Apr.

Cercis canadensis L. 1st bloom 14 Apr.

Malus floribunda Van Houtte 50% bloom 15 Apr.

Syringa vulgaris L. 1st bloom 15 Apr.

Acer platanoides L. 95% bloom 15 Apr.

Aesculus x carnea Hayne 1st bloom 16 Apr.

Malus sargentii Rehder 1st bloom 16 Apr.

Cornus florida L. 50% bloom 17 Apr.

Cercis canadensis L. 50% bloom 17 Apr.

Viburnum x juddii Rehder 95% bloom 17 Apr.

Prunus serrulata Lindley ‘Kwansan’ 1st bloom 19 Apr.

Prunus serrulata Lindley ‘Kwansan’ 50% bloom 19 Apr.

Honeylocust plant bug,

Diaphnocoris chlorionis (Say) Emergence 20 Apr.

Syringa vulgaris L 50% bloom 20 Apr.

Malus sargentii Rehder 50% bloom 20 Apr.

Prunus serrulata Lindley ‘Kwansan’ 95% bloom 20 Apr.
205

Birch leafminer,

Fenusa pusilla (Lepeletier) Emergence 21 Apr.

Malus floribunda Van Houtte 95% bloom 21 Apr.

Cercis canadensis L. 95% bloom 21 Apr.

Cornus florida L. 95% bloom 22 Apr.

Hawthorn lace bug,

Corythucha cydoniae (Fitch) Emergence 22 Apr.

Malus sargentii Rehder 95% bloom 23 Apr.

Oystershell scale,

Lepidosaphes ulmi (L.) Egg hatch 23 Apr.

Yellow poplar weevil,

Odontopus calceatus (Say)a Emergence 23 Apr.

Syringa vulgaris L. 95% bloom 24 Apr.

Lilac/ash borer

Podosesia syringae (Harris) 1st flight 24 Apr.

Crataegus viridis L. ‘Winter King’ 1st bloom 27 Apr.

Cornus kousa (Hance) 1st bloom 28 Apr.

Chionanthus virginicus L. 1st bloom 28 Apr.

Aesculus x carnea Hayne 50% bloom 29 Apr.

Pine needle scale,

Chionaspis pinifoliae (Fitch) Egg hatch 30 Apr.

Pyracantha coccinea Roemer 50% bloom 08 May

Cladrastris kentukea Rudd 1st bloom 08 May

Ilex opaca Aiton, R. 1st bloom 08 May

Lonicera tatarica L. 95% bloom 11 May

Ilex opaca Aiton, R. 50% bloom 11 May

Cladrastris kentukea Rudd 50% bloom 12 May

Cornus kousa (Hance) 95% bloom 13 May

Pyracantha coccinea Roemer 95% bloom14 

May

Juniper scale,

Carulaspis juniperi (Bouché) Egg hatch 15 May

Ilex opaca Aiton, R. 95% bloom 17 May

Cladrastris kentukea Rudd 95% bloom 17 May

Bag worm,

Thyridopteryx ephemeraeformis 

(Haworth) Egg hatch 18 May

Dogwood borer,

Synanthedon scitula (Harris) 1st flight 20 May

Crataegus phaenonpyrum (L. f.) 

Medicus 1st bloom 21 May
206

Bronze birch borer,

Agrilus anxius Gory Emergence 22 May

Tilia cordata Miller 1st bloom 23 May

Syringa reticulata (Blume) 1st bloom 23 May

Catalpa speciosa Warder 1st bloom 24 May

Calico scale,

Eulecanium cerasorum Cockerell) Egg hatch 24 May

Crataegus phaenonpyrum (L. f.) Medicus

50% bloom 24 May

Hydrangea quercifolia Bartrum 1st bloom 27 May

Catalpa speciosa Warder 50% bloom 27 May

Flatheaded appletree borer,

Chrysobothris femorata (Oliver) Emergence 28 May

Crataegus phaenonpyrum (L. f.) Medicus 95% bloom 28 May

Syringa reticulata (Blume) 50% bloom 29 May

Twolined chestnut borer,

Agrilus bilineatus (Weber) Emergence 29 May

Magnolia grandiflora L. 1st bloom 30 May

Catalpa speciosa Warder 95% bloom 31 May

Syringa reticulata (Blume) 95% bloom 02 Jun.

Hydrangea quercifolia Bartrum 50% bloom 02 Jun.

Japanese beetle,

Popillia japonica Newman 1st flight 04 Jun.

Tilia cordata Miller 50% bloom 07 Jun.

Honeylocust borer, 

Agrilus difficilis Gory Emergence 07 Jun

Hydrangea quercifolia Bartrum 95% bloom 07 Jun.

Magnolia grandiflora L. 50% bloom 08 Jun.

Walnut scale,

Quadraspidiotus juglansregiae 

(Comstock) Egg hatch 09 Jun.

Tilia cordata Miller 95% bloom 09 Jun.

Cottony maple leaf scale,

Pulvinaria acericola 

(Walsh & Riley)a Egg hatch 11 Jun.

Koelreuteria paniculata Laxmann 1st bloom 16 Jun.

Abelia x grandiflora (André) Rehder 1st bloom 23 Jun.

Koelreuteria paniculata Laxmann 50% bloom 24 Jun.

Koelreuteria paniculata Laxmann 95% bloom 26 Jun.

Obscure scale,

Melanaspis obscura (Comstock) Egg hatch 06 Jul.

aNot a common name approved by the Entomological Society of America.
207

Table 6. Phenological sequence of 10 insects and 25 plant events common to the present study and Herms (1990) study in Michigan.

Kentucky sequence Michigan sequence

Insects

Plants

Avg.

date

Insects

Plants

Avg. 

date

Acer saccharinum L. (1st) 18 Feb. Acer saccharinum L. (1st) 29 Mar.

Cornus mas L. (1st) 21 Feb. Cornus mas L. (1st) 09 Apr.

Forsythia x intermedia Zabel (1st) 12 Mar. Eastern tent caterpillar, Malacosoma americanum (F.) 09 Apr

Eastern tent caterpillar, Malacosoma americanum (F.) 16 Mar. Forsythia x intermedia Zabel (1st) 16 Apr.

Magnolia stellata (Seibold & Zaccarini) (1st) 19 Mar. Magnolia stellata (Seibold & Zaccarini) (1st) 18 Apr.

Rhododrendron ‘PJM’ (1st) 22 Mar. Acer platanoides L. (1st) 23 Apr.

Pyrus calleryana Decaisne ‘Bradford’ (1st) 23 Mar. Rhododrendron ‘PJM’ (1st) 27 Apr.

Amelanchier arborea (Michaux, F.) Fernald (1st) 05 Apr. Pyrus calleryana Decaisne ‘Bradford’ (1st) 28 Apr.

Acer platanoides L. (1st) 06 Apr. Amelanchier arborea (Michaux, F.) Fernald (1st) 30 Apr.

Viburnum x juddii Rehder (1st) 10 Apr. Cercis canadensis L. (1st) 04 May

Malus floribunda Van Houtte (1st) 11 Apr. Birch leafminer, Fenusa pusilla (Lepeletier) 05 May

Cercis canadensis L. (1st) 14 Apr. Prunus serrulata Lindley ‘Kwansan’ (1st) 06 May

Syringa vulgaris L. (1st) 15 Apr. Viburnum carlesi (1st) 06 May

Malus sargentii Rehder (1st) 16 Apr. Malus floribunda Van Houtte (1st) 07 May

Prunus serrulata Lindley ‘Kwansan’ (1st) 19 Apr. Syringa vulgaris L. (1st) 09 May

Birch leafminer Fenusa pusilla 21 Apr. Malus sargentii Rehder (1st) 10 May

Oystershell scale, Lepidosaphes ulmi (L.) 23 Apr. Lonicera tatarica L. (1st) 12 May

Viburnum plicata var. tomentosum (Thunberg) (1st) 24 Apr. Viburnum plicata var. tomentosum (Thunberg) (1st) 16 May

Lilac/ash borer, Podosesia syringae (Harris) (1st) 24 Apr. Lilac/ash borer, Podosesia syringae (Harris) (1st) 17 May

Lonicera tatarica L. (1st) 25 Apr. Oystershell scale, Lepidosaphes ulmi (L.) 20 May

Crataegus viridis L. ‘Winter King’ (1st) 27 Apr. Crataegus viridis L. ‘Winter King’ (1st) 25 May

Chionanthus virginicus L. (1st) 28 Apr. Chionanthus virginicus L. (1st) 28 May

Juniper scale, Carulaspis juniperi (Bouché) 15 May Bronze birch borer, Agrilus anxius Gory 02 Jun.
208Bronze birch borer, Agrilus anxius Gory 22 May Crataegus phaenonpyrum (L. f.) Medicus (1st) 12 Jun.

Crataegus phaenonpyrum (L. f.) Medicus (1st) 21 May Juniper scale, Carulaspis juniperi (Bouché) 12 Jun.

Syringia reticulata (Blume) (1st) 23 May Syringia reticulata (Blume) (1st) 13 Jun.Tilia cordata Miller (1st) 23 May Catalpa speciosa Warder (1st) 15 Jun.Catalpa speciosa Warder (1st) 24 May Crataegus phaenonpyrum (L. f.) Medicus (1st) 18 Jun.Crataegus phaenonpyrum (L. f.) Medicus (95%) 28 May Tilia cordata Miller (1st) 22 Jun.Tilia cordata Miller (95%) 09 Jun. Cottony maple leaf scale, Pulvinaria acericola (Walsh & 

Riley)

25 Jun.

Cottony maple leaf scale, Pulvinaria acericola (Walsh & Riley) 11 Jun. Tilia cordata Miller (95%) 27 Jun.Koelreuteria paniculata Laxmann (1st) 26 Jun. Koelreuteria paniculata Laxmann (1st) 05 Jul.

Designation in parentheses after plant names refers to 1st or 95% bloom.aNot a common name approved by the Entomological Society of America.

Tables 4 and 6 modified and reproduced with permission from the Journal of Economic Entomology, granted April 2, 2009.
209

piercing and sucking insects and mites
210
211

Spruce Spider Mite

Oligonychus ununguis (Jacobi)

Acari: Tetranychidae

Richmond, D. S.; Shetlar, D. J. 1996. Eclosion time and spatial distribution of overwintering 

spruce spider mite (Acari: Tetranychidae) eggs on Colorado spruce. Journal of Economic 

Entomology 89: 447-452.

Objectives

To develop a degree-day model for the ecolosion of diapausing O. ununguis eggs; to determine the spatial distribution of eggs on Colorado spruce, Picea pungens.

Abstract

The spruce spider mite, Oligonychus ununguis (Jacobi), can be a significant pest of field, 

nursery, and greenhouse-grown plants. Coniferous hosts include Fraser fir [Abies fraseri

(Pursh)], pines (Pinus spp.), junipers (Juniperus spp.), and several spruces (Picea spp.). Risk 

of mite damage is especially high during periods of drought and high temperatures. Under 

these conditions, mite populations build quickly and can cause serious damage. Infested 

needles are stippled and covered with webbing; eventually they turn brown and fall prematurely from infested trees. Growth loss and mortality may follow. Ornamental nursery crops 

are particularly susceptible to aesthetic damage by O. ununguis and even low mite densities 

can result in economic loss. Pest managers would benefit from being able to time chemical 

applications for O. ununguis with the eclosion of winter-diapausing eggs, or direct applications to areas where eggs are laid rather than treating the entire plant.

Research conducted on Colorado spruce, Picea pungens Engelmann, in Ohio indicated that 

50% of overwintering eggs of O. ununguis hatched at approximately 170 degree-days (DD) 

measured using a base temperature of 5.6oC beginning March 1, and growers should treat the 

first generation around 258DD when eclosion reaches 100%. A study of the spatial distribution of overwintering eggs showed that numerically greater numbers of O. ununguis eggs 

were found in the mid-crown level but no eggs were found on the trunks. 

On south- and east-facing sides of Colorado spruce, significantly greater numbers of eggs 

were found on branch axes away from the trunk. On branches on the north- and west-facing 

sides, eggs were located closer to the trunk along branch axes. Mean egg location along 

branch axis also varied with crown level. Eggs were located farther away from the trunk on 

branches in the lower third of the crown as compared to branches in the middle and upper 

thirds. The spatial distribution of O. ununguis eggs likely reflects desiccation by prevailing 

winter winds.
212

Sampling Procedure

Monitor degree-day accumulation from March 1 using the base temperature of 5.6 ̊C. Predict 

the percentage of eclosion by overwintering eggs using the formula:

probit Y = –28.64 + 6.59 ln X

where X = the natural log of accumulated degree-days from base 5.6 ̊C and Y = cumulative 

% eclosion in probits. Consider treating trees for first generation O. ununguis around 258DD 

from March 1. Make certain that sprays penetrate the foliage on the lower third of Colorado 

spruce, where eggs are laid on limbs close to the trunk on the east- and south-facing sides. 

Trunks do not require treatment.

Notes

These recommendations are derived from research conducted on Colorado spruce in Ohio 

and may not be applicable to other conifers or in other regions. Use these recommendations 

with caution until validated for other hosts and in other regions
213

Spruce Spider Mite

Oligonychus ununguis (Jacobi)

Acari: Tetranychidae

Shrewsbury, P. M.; Hardin, M. R. 2004. Beat sampling accuracy in estimating spruce spider 

mite (Acari: Tetranychidae) populations and injury on juniper. Journal of Economic Entomology 97: 1444-1449.

Objectives

To relate a method of subsampling O. ununguis to its absolute population density; to relate O. 

ununguis feeding injury to estimated population density.

Abstract

The spruce spider mite, Oligonychus ununguis (Jacobi), can be a significant pest of field, 

nursery, and greenhouse-grown plants. Coniferous hosts include Fraser fir [Abies fraseri

(Pursh)], several spruces (Picea spp.), and in the case of the current study, juniper (Juniperus 

chinensis A. Henry). In the field, risk of mite damage is especially high during periods of 

drought and high temperatures. Under these conditions, mite populations build quickly and 

can cause serious damage. Infested needles are stippled and covered with webbing; eventually they turn brown and fall prematurely from infested trees.

A beat sampling method for estimating O. ununguis populations on Juniperus chinensis was 

related to an absolute population measure as well as feeding injury on its host. The log of 

spruce spider mite density estimated from the beat sample was related positively to the log 

of total O. ununguis density (r2

 = 0.80) and to plant injury level (r2 = 0.43). These findings 

may allow managers to predict total mite density and feeding injury on infested trees using 

simple beat counts. These findings may apply to O. ununguis populations on other plants 

with growth patterns similar to juniper. Moreover, knowledge of total mite density from a 

subsample may facilitate evaluation of biological control success and allow managers to better adjust predator release rates according to mite population density.

Sampling Procedure

Randomly select an appropriate number of trees to sample in the area of concern. Use a 0.5 

m (1.6 ft.) wooden dowel to beat a 3,540 cm3

 (0.13 ft.3) area of foliage in the lower, outer 

crown of each juniper sampled. Always select the sample area at the same location of each 

tree sampled. Place a 10 x 14 cm (4 x 5.5 in.) sheet of graph paper on a clipboard beneath 

the foliage. Rap the foliage sample unit with the dowel 10 times to dislodge mites onto the 

paper. Count all mites present on the paper. 
214

To estimate total mite density, enter the total number of mites sampled using the beat method 

into the equation:

X = (Log10 sum of mites + 0.12)/0.798

then calculate the antilog of X to obtain the estimated total number of mites as the authors 

calculated the regression equation using log10 transformed data.

To estimate percent plant injury, enter counts obtained from the beat method into the equation:

Percent plant injury = 5.37 + 0.0007X

where X = sum of mites in beat sample.

Notes

The standard error of each regression, which was not presented, would give a range of total density and percent plant injury to expect for a given mite count obtained through beat 

sampling. This estimate of standard error would allow users to determine if the range of total 

mite density and/or percent plant injury level included their specific damage or action threshold for O. ununguis. The authors cautioned that beat sampling underestimates the density of 

mites, which must be considered when considering the release of predatory mites for control 

of O. ununguis
215

Spruce Spider Mite

Oligonychus ununguis (Jacobi)

Acari: Tetranychidae

Webster, R. 1993. Sample collection method and sequential sampling plan for mites (Oligonychus ununquis) and aphids (Cinaria laricifex) on tamarack. Tech. Note No. 278. 

Forestry Canada, Maritimes Forest Research Center; 5 p.

Objective

To propose a fixed precision sequential sampling plan to determine the optimal number of 

samples needed to monitor populations of O.ununquis on larch.

Abstract

Spruce spider mite, Oligonychus ununquis (Jacobi), is a common pest of spruce, Picea spp., 

and other conifers. Infested needles are covered with webbing, turn yellow, and drop prematurely. High population densities may cause a loss of tree vigor. Infestations are a recurrent 

problem in seed orchards in New Brunswick.

A sequential sampling plan with fixed precision was developed for O. ununquis on larch, 

Larix spp. Depending on the desired level of precision, 4 to 50 trees need to be sampled using this plan. Known threshold levels can be applied to this sampling plan to determine how 

many branch samples are necessary to determine if control treatments are warranted. Counting up to 1,000 mites requires 10¬–15 minutes.

Sampling Procedure

Randomly select at least 25 larch trees in each stand being surveyed. Sample one branch per 

tree, beating each branch with a 60-cm long padded stick ten times in rapid succession to dislodge any O. ununquis present into a 30 x 20 x 8 cm container held under the branch. Wash 

the mites out of the collection container with 35–40 ml of Oudeman’s solution (8.7 L of 70% 

alcohol, 0.5 L glycerine, 0.8 L glacial acetic acid to make 10 L of solution) and into a 50-ml 

screw cap vial. Oudeman’s solution kills and preserves all mites in the sample with their appendages extended, allowing for identification at a later date.

Remove needles, large pieces of debris, and excess Oudeman’s solution from the sample. 

Pour the remainder into a petri dish set on a 1 cm grid to facilitate counting. It takes about 

2–4 minutes to prepare each sample for counting.

Referencing Fig. B, count the cumulative number of O. ununquis in each sample until the 
216

stop line is crossed for the desired level of sampling precision. A precision level of 0.25 is 

generally sufficient for most purposes. For higher levels of precision, more samples should 

be taken from >25 trees, especially if O. ununquis is present at low densities.

Note

This is a proposed sampling plan that still requires validation. Use with caution.

Figure A. Sequential sampling plan for O. ununquis on larch based on Taylor’s power law. 

The sequential sample stop lines were calculated from the formula of Green (1970) based on 

the regression from Taylor’s (1961) power law. Sequential sample stop lines are given for 

precision levels of D1

 = 0.15, D2 = 0.20, D3 = 0.25, D4 = 0.30, and D5 = 0.35.

Figure A reproduced with the permission of the Minister of Public Works and Government Services, granted April 14, 2009.
217

Balsam Woolly Adelgid

Adelges piceae (Ratzeburg)

Hemiptera: Adelgidae

Bryant, D. G. 1976a. Sampling populations of (Homoptera: Phylloxeridae) on balsam fir, 

Abies balsamea. Canadian Entomologist 108: 1113-1124.

Objective

To describe methods of detecting, monitoring, and estimating populations of A. piceae.

Abstract

The balsam woolly adelgid, Adelges piceae (Ratzeburg), is an introduced species first recorded in North America in Maine in 1908. It has spread throughout the native range of balsam 

fir, Abies balsamea (L.) Mill., and Fraser fir, A. fraseri (Pursh) Poir., and is also found in the 

Pacific Northwest. Trees suffering from extensive stem attacks have characteristic white 

woolly masses on the stem and die quickly. Methods of detecting, monitoring and measuring population levels of A. piceae were developed on balsam fir sampled in Newfoundland, 

Canada.

The recommended sampling unit used to measure populations of sessile A. piceae is a second 

position node from a secondary branch axis, such as nodes 8, 12, 17, or 30 (Bryant 1972, 

1976b). Sampling should be done at these positions on non-flowered, 3-yr-old branch tips of 

balsam fir. In addition, the author provides a thorough description of the morphology of the 

stem and crown of balsam fir, including surface features of the bark. 

Sampling Procedure

Detection survey: Reference Table IV to determine the number of locations in a stand of balsam fir and number of nodes to sample at each location. Randomly select branches throughout the canopy of balsam fir. Use a 6 to 10X hand lens to examine underneath the bud scales 

of nodes 8, 12, 17, or 30 for the presence of sessile A. piceae. Refer to Fig. 2 for a diagram 

illustrating the node positions (Bryant 1976b). If no adelgids are present on any nodes in the 

sample, it is safe to classify the location as uninfested with 95% confidence. In Newfoundland, usually one location is sampled for every 10 ha of forest. This sampling intensity has 

not been verified statistically and would depend heavily on the distribution of A. piceae at the 

stand and forest level, which is not known.

Monitoring Populations: This method is used once an infestation is detected. Use a 6 to 
218

10X hand lens to examine underneath the bud scales of nodes 8, 12, 17, or 30 for the presence of sessile A. piceae. Refer to Fig. 2 for a diagram illustrating the node positions (Bryant 

1976b). Generally, if <33% of the examined nodes are infested, then A. piceae populations 

do not warrant control measures. However, if >33% of the nodes are infested then more 

detailed sampling is required to determine if control measures are needed. Refer to Table IV 

to determine the appropriate sampling intensity.

Measuring Population Levels: Precise sampling methods are often needed to estimate A. 

piceae population levels annually, study its population dynamics, or to evaluate the success 

of treatments. The required number of sample units was estimated for low (Table V) and 

high (Table VI) populations at selected levels of precision, a range of allowable error, and a 

range of levels of assurance. Sample trees as described above, except examine nodes for A. 

piceae at 12 to 20X using a stereomicroscope.

Note

The sampling methods described here are only applicable to sessile adelgids and not eggs 

and crawlers. Evaluation of appropriate nodes might be difficult under certain circumstances. 

Interested users should refer to Bryant (1972, 1976b) for further details regarding the sample 

unit. Tables V and VI were developed from populations of A. picea on grand fir, Abies grandis (Dougl. ex D. Don) Lindl., in British Columbia. They should be used with caution for A. 

picea populations on different hosts in eastern North America. The author does not clarify 

the distinction between levels of confidence and levels of assurance; this might be clarified 

by consulting Steel and Torrie (1960).

References

Bryant, D. G. 1972. The measurement of population density of the balsam woolly adelgid, 

Adelges piceae (Ratz.) (Homoptera: Phylloxeridae) a highly aggregate species. PhD dissertation, Yale University. 169 p.

Bryant, D. G. 1976b. Distribution, abundance, and survival of the balsam woolly aphid, 

Adelges piceae (Homoptera: Phylloxeridae), on branches of balsam fir, Abies balsamea. 

Canadian Entomologist 108: 1097-1111.

Steel, R. G. D.; Torrie, J. H. 1960. Principles and procedures of statistics. McGraw-Hill, New 

York. 481 p.
219

Fig. 2. Illustration of a 5-year-old branch showing four branching axes, five node positions, numbered nodes on the main branchings, and the ventral (v) and internodal (i) twigs.

1

4

13

40

121

3

12

10

9

11

i

i

i

v

v

v

80

39

3132 33

28

29

27

35 38

36

37

34

30

Fourth

Fifth

First

Second

Third

Primary

2

Secondary

8

7

5

6

Tertiary

17

25

26

24 23 22

15

14

16

21 20

19

18

Quaternary

Positions: Axes:
220

Table IV. The number (n) of node 8 (or 12, 17, or 30) samples required per sample location (m) to 

state with over 95% confidence (α) that a balsam fir stand is uninfested by A. piceae where α = (1-qn

)m 

and q = 0.53, probability of zero counts in an aphid population (Bryant 1972).

No. of sample locations 

in the stand

No. of nodes (one per tree) 

per location

1 5

2 6

3–5 7

6–10 8

11–16 9

Table V. Number of sample units (node 8 or its homologue 12, 17 or 30) required in a low 

population level of aphids to estimate each mean ([ ∑ log (Xi

 +1)]N-1) with a confidence of 

90 or 95% at an allowable error of 10 to 33% of the mean with an assurance of 0.75, 0.90, or 

0.95. s2

 = 0.042904 at d.f. = 48.

 Estimate one mean Compare two means Estimate one mean Compare two means

Allowable

error

Level of assurance Level of assurance

.75 .90 .95 .75 .90 .95 .75 .90 .95 .75 .90 .95

90% confidence 95% confidence

.03 166 187 199 313 378 410 228 265 283 457 500 546

.05 59 74 84 117 136 152 92 105 112 170 191 201

.07 31 38 41 60 71 86 44 54 57 72 84 113

.10 18 22 24 31 38 41 24 29 31 44 53 57 a

 Nominally 10%, 15%, 25%, and 33% of the mean.

Table V. Number of sample units (node 8 or its homologue 12, 17 or 30) required in a low population level of 

aphids to estimate each mean ([ ∑ log (Xi +1)]N-1) with a confidence of 90 or 95% at an allowable error of 10 

to 33% of the mean with an assurance of 0.75, 0.90, or 0.95. s2 = 0.042904 at d.f. = 48.

 Estimate one mean Compare two means Estimate one mean Compare two means

Allowable

error

Level of assurance Level of assurance

.75 .90 .95 .75 .90 .95 .75 .90 .95 .75 .90 .95

90% confidence 95% confidence

.08 138 163 177 271 302 332 192 222 244 375 428 466

.11 79 94 102 148 172 184 109 119 131 199 237 258

.20 26 33 36 46 35 59 38 41 44 64 82 93

.26 18 20 21 32 37 39 23 29 32 39 47 52

a Nominally 10%, 15%, 25%, and 33% of the mean.

Figure 2 and Tables IV, V, and VI are reprinted as fair use under 17 USC § 107.
221

Hemlock Woolly Adelgid

Adelges tsugae Annand

Hemiptera: Adelgidae

Costa, S.; Onken, B. 2006. Standardizing sampling for detection and monitoring of hemlock 

woolly adelgid in eastern hemlock forests. FHTET-2006-16. Morgantown, WV: U.S. Department of Agriculture, Forest Service, Forest Health Technology Enterprise Team; 11 p.

Objective

To develop an efficient, standardized sampling method for determining the infestation level 

of A. tsugae within a stand (not at the individual tree level) by determining the percentage of 

infested trees.

Abstract

Hemlock woolly adelgid, Adelges tsugae (Annand), is a severe pest of eastern hemlock 

[Tsuga canadensis (L.) Carr.] and Carolina hemlock (Tsuga caroliniana Engelm.) on the east 

coast of the USA and Canada. Infestation by A. tsugae results in reduced production of new 

foliage, premature needle drop, branch dieback, thinning of the crown, and a general decline 

resulting in the death of the tree. Two parthenogenetic generations of A. tsugae, the sistentes 

and progredientes, occur each year. Eggs of both generations hatch into a mobile crawler 

stage that settles at the base of a needle to feed and mature. Sistentes are found on hemlock 

from mid-summer into spring. Progredientes occur for a brief period during late spring and 

early summer, giving rise to the next generation of sistentes that aestivates over the summer 

months and resumes feeding in the fall. Infestations may spread quickly, and A. tsugae is 

expected to spread throughout the range of eastern and Carolina hemlock. 

Cutoff thresholds to stop sampling (i.e., stop thresholds) were developed for binomial sampling of adelgids based on the optimum sample sizes needed to reach a precision level of 

0.25. This allows the detection of A. tsugae with 75% reliability in hemlock stands where 

less than 2% of the trees are infested (Table 1). The precision level for this plan is consistent for stands with at least 14% of the trees infested with adelgids. Lower infestation levels 

require more trees to be sampled with little additional information gained in return. No more 

than 100 trees need be sampled using this plan, and substantially fewer trees need be sampled 

if the infestation level is high within the stand. 

Sampling Procedure

Roughly divide the stand into 4 blocks large enough to avoid overlap (ideally 1.6 hect-
222

ares each). Select trees that have branches within an arm’s reach of the ground and avoid 

branches lacking needles. Arbitrarily select a tree as the initial sampling point in the center 

of the first block. Examine the underside of the terminal meter of one branch for evidence of 

ovisacs. Infestation is judged on the presence or absence of ovisacs and there is no need to 

count the number of ovisacs observed. If adelgids are present, mark the datasheet with a 1 to 

indicate the presence of adelgids. Consult the sampling datasheet for a list of semi-random 

cardinal directions and take 25 paces (2 strides per pace) in the designated direction to the 

next sampling point. Select the nearest suitable tree at the sampling point and examine it. 

If adelgids are found on the second tree, mark the datasheet with a 2. Re-enter the last tally 

number with 1 if no adelgids are found on the second tree. The datasheet uses a cumulative 

tally of all infested trees. Continue sampling until reaching the appropriate stop level or 25 

trees have been sampled in that block.

If no adelgids are detected on the first tree, search another branch on the opposite side of 

the tree in the same manner. If no adelgids are detected on the second branch, mark the 

datasheet with a 0. Continue to the next tree using the semi-random cardinal directions on 

the datasheet as described above. If A. tsugae is not found on the first 8 trees, continue sampling trees until A. tsugae is found or 100 trees have been sampled. After A. tsugae is found, 

but the tally count is less than the stop threshold, continue sampling trees and adding new 

blocks as needed after each set of 25 trees. Stop sampling if 1) the tally count is below the 

stop threshold after sampling 100 trees or 2) the tally count is greater than the stop threshold. 

Calculate the level of stand infestation as:

Percent infested trees = (number of infested trees) / (number of trees examined) X 100

Notes

The directions for determining stand infestation level are more appropriate for large stands 

of 4 ha or more, but they can be used for smaller stands with a smaller hemlock component. 

Stands do not need be divided into precise blocks and the semi-random cardinal directions 

listed on the datasheet do not have to be followed precisely. Small or narrow stands, or 

stands with few hemlocks, can be sampled in any pattern that provides a random, thorough 

sample.

There is no discrimination between dead or living A. tsugae when classifying a tree as infested using this plan. Dead A. tsugae indicates that the tree was previously infested and the 

infestation will likely persist in the area. Trees should be sampled when the ovisacs can be 

observed easily, from late October into mid-July.
223

Table 1. Maximum number of trees that must be examined to detect an infested tree by 

minimum detection threshold (minimum percent of infested trees) and reliability level (probability of finding a single infested tree). The shaded area encompasses the recommended 

100-tree sample.

Minimum %

Infested Trees in 

Stand

Reliability Level (%)

50 75 95 99

0.5 138 277 598 919

1 69 138 298 458

2 34 69 148 228

3 23 46 98 151

5 14 27 58 90

10 7 13 28 44

20 3 6 13 21
224Hemlock Woolly Adelgid Sampling Datasheet

Date: Location: Sampler: Comments on reverse side

Path

Direction

Tree

Sum

HWA

Trees

STOP≥

Threshold

Direction Tree

Sum

HWA

Trees

STOP≥

Threshold

Direction Tree

Sum 

HWA

Trees

STOP≥

Threshold

Block 1 1 n/a SW 35 11 N 69 13

NE 2 n/a W 36 11 N 70 13

SE 3 n/a SW 37 11 E 71 13

N 4 n/a NW 38 11 SE 72 13

NW 5 n/a NW 39 11 NE 73 13

SW 6 n/a NE 40 11 SE 74 13

S 7 n/a N 41 12 SW 75 13

S 8 8 SW 42 12 Block 4 76 13

SE 9 8 W 43 12 N 77 13

SW 10 8 NW 44 12 E 78 13

W 11 8 SW 45 12 N 79 13

S 12 8 W 46 12 SE 80 13

E 13 8 SE 47 12 SW 81 13

E 14 8 S 48 12 S 82 13

NE 15 8 E 49 12 SW 83 13

SE 16 8 S 50 12 S 84 13

NE 17 8 Block 3 51 12 W 85 13

E 18 8 NE 52 12 N 86 13

N 19 9 E 53 12 W 87 13

NW 20 9 S 54 12 W 88 13

N 21 9 SE 55 12 NE 89 13

W 22 9 SE 56 12 N 90 13

S 23 9 S 57 13 N 91 14
225

W 24 10 W 58 13 NW 92 14

W 25 10 NW 59 13 N 93 14

Block 2 26 10 S 60 13 SE 94 14

E 27 10 NW 61 13 NE 95 14

SE 28 10 NE 62 13 N 96 14

NE 29 10 W 63 13 E 97 14

SE 30 10 NW 64 13 SE 98 14

NE 31 11 N 65 13 E 99 14

E 32 11 NE 66 13 NE 100 14

N 33 11 NE 67 13 STOP

N 34 11 W 68 13
226

Hemlock Woolly Adelgid

Adelges tsugae Annand

Hemiptera: Adelgidae

Fidgen, J. G.; Legg, D. E.; Salom, S. M. 2006. Binomial sequential sampling plan for hemlock woolly adelgid (Hemiptera: Adelgidae) sistens infesting individual eastern hemlock 

trees. Journal of Economic Entomology 99: 1500-1508.

Objective

To develop an efficient, cost-effective sampling method for determining the infestation level 

of A. tsugae on individual, high-value eastern hemlock trees.

Abstract

Hemlock woolly adelgid, Adelges tsugae (Annand), is a severe pest of eastern hemlock 

[Tsuga canadensis (L.) Carr.] and Carolina hemlock (Tsuga caroliniana Engelm.) on the east 

coast of the U.S. and Canada. Infestation by A. tsugae results in reduced production of new 

foliage, premature needle drop, branch dieback, thinning of the crown, and a general decline 

resulting in the death of the tree. Two parthenogenetic generations of A. tsugae, the sistentes 

and progredientes, occur each year. Eggs of both generations hatch into a mobile crawler 

stage that settles at the base of a needle to feed and mature. Sistentes are found on hemlock 

from mid-summer into spring. Progredientes occur for a brief period during late spring and 

early summer, giving rise to the next generation of sistentes that aestivates over the summer 

months and resumes feeding in the fall. Infestations may spread quickly, and A. tsugae is 

expected to spread throughout the range of eastern and Carolina hemlock. 

A binomial sequential sampling plan was developed that samples the presence of sistentes on 

new shoots in a non-destructive manner. Sistentes are present on the tree for a longer period 

of time and are more easily observable than the progredientes. The plan requires examination of 20 to 80 new shoots on each tree to classify the tree as having a low, high, or indeterminate infestation level. Sampling took <2 min for each tree.

Sampling Procedure

Select eastern hemlocks that are in good health and exhibiting few symptoms of decline. 

Sample sistentes between October and May. Choose a threshold level appropriate for the 

objective: a 10% level is appropriate for maintaining aesthetics while a 30% level is appropriate if the goal is to maintain tree health.
227

Randomly select four branches in the lower crown of the tree. Branches do not need to be 

cut from the tree, but use pole pruners to reach the lower third of the crown if no live branches are available in the lower crown. There should be at least 20 new shoots on each branch 

sampled. Tally the number of the first five shoots on each branch that have at least one adelgid. Sum the number of infested shoots across all four branches (number of infested shoots 

out of 80 shoots sampled).

Check Table 1 for the upper and lower stop values associated with the threshold level chosen 

for your objective. Classify the tree as a low infestation if the tally of infested shoots is less 

than the lower stop value. Classify the tree as a high infestation if the tally of infested shoots 

is greater than the upper stop value. 

If the tally falls between the lower and upper stop values, continue sampling the tree. Examine the next set of five shoots on each branch and tally the number of shoots with at least one 

adelgid on them. Add this cumulative tally to the first tally and refer to Table 1 again. If the 

cumulative tally for 40 samples is less than the lower stop value, classify the tree as a low 

infestation. If the cumulative tally for 40 samples is greater than the upper stop value, classify the tree as a high infestation. If the tally falls between the lower and upper stop values 

listed for 40 samples, count the number of infested shoots within the third set of five shoots 

on each branch. Continue in this manner until the tree is classified as either a low or high 

infestation or a total of 80 shoot samples have been examined. If the tree cannot be classified 

as a low or high infestation after examining a total of 80 shoot samples, then classify the tree 

as intermediate. Trees classified as low or intermediate using this plan may not require a pest 

management action.

Notes

Densities of A. tsugae on new shoots did not differ between the lower crown and the middle 

crown of infested trees in this study. This sampling plan is appropriate for building populations of A. tsugae but before infested trees are in decline. Sampling trees already in decline 

or sampling the progredientes stage has not been validated statistically and will likely produce inconsistent results.
228

Table 1. Stop boundaries for binomial sequential sampling of A. tsugae infesting eastern 

hemlock at the 10 and 30% of new shoots infested with at least one sistens thresholds

Threshold

(%)

Sample

no.

Stop boundaries

Low infestation

Lower 

stop value

Continue sampling

Upper

stop value

High infestation

10 20

40

60

80

30 20

40

60

80

Table 1 reproduced with permission from the Journal of Economic Entomology, granted April 2, 2009.
229

Black Larch Aphid

Cinaria laricifex (Fitch)

Hemiptera: Aphididae

Webster, R. 1993. Sample collection method and sequential sampling plan for mites (Oligonychus ununquis) and aphids (Cinaria laricifex) on tamarack. Tech. Note No. 278. Forestry 

Canada, Maritimes Forest Research Center; 5 p.

Objective

To propose a fixed precision sequential sampling plan to determine the optimal number of 

samples needed to monitor populations of C. laricifex.

Abstract

Black larch aphid, Cinaria laricifex (Fitch), is a common pest of new foliage on larch, Larix

spp. Infested needles turn yellow and drop prematurely. High population densities may cause 

a loss of tree vigor. Infestations are a recurrent problem in seed orchards in New Brunswick.

A sequential sampling plan with fixed precision was developed for C. laricifex on larch. Depending on the desired level of precision, 12 to 50 trees need to be sampled using this plan. 

Known threshold levels can be applied to this sampling plan to determine how many branch 

samples are necessary to determine if control treatments are warranted.

Sampling Procedure

Randomly select at least 25 larch trees in each stand being surveyed. Sample one branch per 

tree, beating each branch with a 60-cm long padded stick ten times in rapid succession to 

dislodge any aphids present into a 30 x 20 x 8 cm container held under the branch. Wash the 

aphids out of the collection container with 35–40 ml of Oudeman’s solution (8.7 L of 70% 

alcohol, 0.5 L glycerine, 0.8 L glacial acetic acid to make 10 L of solution) and into a 50-ml 

screw cap vial. Oudeman’s solution kills and preserves all aphids in the sample, allowing for 

identification at a later date.

Remove needles, large pieces of debris, and excess Oudeman’s solution from the sample. 

Pour the remainder into a petri dish set on a 1 cm grid to facilitate counting. It takes about 

2–4 minutes to prepare each sample for counting.

Referencing Fig. B, count the cumulative number of aphids in each sample until the stop line 

is crossed for the desired level of sampling precision. A precision level of 0.25 is generally 
230

sufficient for most purposes. For higher levels of precision, more samples should be taken 

from >25 trees, especially if aphids are present at low densities.

Note

This is a proposed sampling plan that still requires validation. Use with caution

Figure B. Sequential sampling plan for aphids (Cinaria sp.) on larch based on Taylor’s 

power law. The sequential sample stop lines were calculated from the formula of Green 

(1970) based on the regression from Taylor’s (1961) power law. Sequential sample stop lines 

are given for precision levels of D = 0.20, 0.25, 0.30, and 0.35.

Figure B reproduced with the permission of the Minister of Public Works and Government Services, granted April 14, 2009.
231

Balsam Twig Aphid

Mindarus abietinus Koch

Hemiptera: Aphididae

Fondren, K. M.; McCullough, D. G. 2003. Phenology and density of balsam twig aphid, Mindarus abietinus Koch (Homoptera: Aphididae) in relation to bud break, shoot damage, and 

value of fir Christmas trees. Journal of Economic Entomology 96: 1760-1769.

Objective

To determine an appropriate spray period for M. abietinus based on pest phenology.

Abstract

Balsam twig aphid, Mindarus abietinus Koch, causes distortion, needle loss, and reduced 

growth on balsam fir, Abies balsamea (L.) Mill., and Fraser fir, Abies fraseri (Pursh) Poir. 

Both tree hosts are grown as Christmas trees with high economic value. The aesthetic or economic damage produced by M. abietinus has not been studied and the true impact of this pest 

may be over-estimated, leading to the unnecessary applications of insecticides for control.

The authors investigated the phenology of M. abietinus and resulting plant injury on balsam 

fir grown in commercial Christmas tree fields. Development of fundatrices and sexuparae 

was correlated with accumulated degree-days (DD) above a base temperature of 10 ̊C. Fundatrices began reproducing by ≈83DD and sexuparae appeared ≈83-111DD, suggesting that 

chemical controls should be applied for M. abietinus between 56-83DD. Fundatrice density 

was not a good predictor of sexuparae density or subsequent shoot damage. In addition, the 

authors attempted to establish an aesthetic injury level for M. abietinus on balsam fir grown 

as Christmas trees, but most polled consumers appeared to accept light to moderate injury on 

the trees. In such cases, slight economic loss may not warrant the cost of chemical control 

for M. abietinus. This differs from the results of Kleintjes et al. (1999), who reported that 

chemical control was warranted on trees with densities of two or more M. abietinus fundatrices.

Sampling Procedure

Beginning in March, monitor degree-day accumulation above the base threshold of 10 ̊C. 

Consider spraying for M. abietinus between 56-83DD, when eggs have completed hatching 

but before fundatrices begin reproducing. Management decisions should take into consideration such factors as expected economic value of trees at harvest, expense of treatments, and 

consumer acceptance of damaged trees.
232

Reference

* Kleintjes, P. K.; Lemoine, E. E.; Schroeder, J.; Solensky, M. J. 1999. Comparison of methods for monitoring Mindarus abietinus (Homoptera: Aphididae) and their potential 

damage in Christmas tree plantations. Journal of Economic Entomology 92: 638-643.
233

Woolly Pine Needle Aphid

Schizolachnus piniradiatae (Davidson)

Hemiptera: Aphididae

Kulman, H. M. 1967. Within-tree distribution and winter mortality of eggs of the woolly pine 

needle aphid, Schizolachnus piniradiatae. Annals of the Entomological Society of America 

60: 384-387.

Objective

To develop a sampling method for overwintering S. piniradiatae eggs as a means of predicting subsequent infestation levels on individual trees.

Abstract

Woolly pine needle aphid, Schizolachnus piniradiatae (Davidson), occurs on numerous species of pine (Pinus spp.). It is widespread in Canada but has also become a pest in the USA. 

A study conducted on 5-year-old, plantation-grown red pines (Pinus resinosa Ait.) in West 

Virginia indicated that trees can be classified as moderately or severely infested by S. piniradiatae based on the percentage of needles bearing egg masses. The percentage of needles 

infested by S. piniradiatae described the infestation level more accurately than the number 

of eggs per needle. Severely infested red pines had at least twice the percentage of infested 

needles than moderately infested trees. No oviposition preference was observed for new, 

1-yr., or 2-yr. needles. This sampling method may aid managers in making control decisions 

for S. piniradiatae.

Sampling Procedure

Select red pines separated by at least 15.24 m in mid-March, before egg hatch begins. From 

both the middle and lower crown, randomly select two branches on opposite sides of each 

tree (four branches per tree). Examine needles on the branches for S. piniradiatae egg 

masses. Classify individual trees as moderately or severely infested based on the percentage 

of needles bearing egg masses.

Percentage of needles bearing eggs Infestation level

10.1–20% moderate

>20% severe

Note

The described infestation levels and aphid distributions may differ among other pine species, 

trees of different age classes, or trees not grown in plantings. This method should be used 

with caution until validate for other pine hosts in other regions.
234

Saratoga Spittlebug

Aphrophora saratogensis (Fitch)

Hemiptera: Cercopidae

Wilson, L. F. 1971. Risk-rating Saratoga spittlebug damage by abundance of alternate-host 

plants. Res. Note NC-10. St. Paul, MN: U.S. Department of Agriculture, Forest Service, 

North Central Forest Experiment Station; 4 p.

Objective

To describe a risk-rating system for A. saratogensis damage based on the abundance of alternate host plants.

Abstract

Saratoga spittlebug, Aphrophora saratogensis (Fitch), is a pest of red pine, Pinus resinosa

Ait, in the Great Lakes region of North America, and occasionally of jack pine, Pinus banksiana Lamb., and Scots pine, Pinus sylvestris L.. Adult A. saratogensis feed on host pine 

shoots, favoring young trees under 5 m tall. Feeding injury results in dead branches, deformed trees, topkill, and mortality. Nymphs feed on alternate hosts near host pines, with 

sweet fern, Comptonia peregrina (L.), being the favored host plant (Table 1). 

Potential damage by A. saratogensis on red pines can be predicted by the abundance of sweet 

fern and alternate host plants within the stand. Stands can be evaluated either before planting 

or after establishment. Young host trees are more susceptible to damage, and trees >3 m tall 

are considered resistant to damage by A. saratogensis. Protect stands rated as moderate or 

heavy in areas where A. saratogensis occurs by removing sweet fern and other alternate host 

plants. 

Sampling Procedure

The risk-rating procedure assumes that A. saratogensis is either present in the stand or in the 

surrounding areas, that the stand is stocked with >500 trees per hectare, and the site index is 

≥50. In general, stands with red pine >3 m tall are not susceptible to damage by A. saratogensis.

Stands with uniform vegetative ground cover can be rated as one contiguous patch. Stands 

with very patchy vegetation should be divided into smaller areas for rating; in such cases, 

consider rating each 0.2 hectares. Estimate the percentage of ground in the stand covered 

by sweet fern. Estimate the percentage of ground covered by other hosts accepted by A. 

saratogensis (Table 1). Do not include non-host plants or bare soil. To determine the risk of 
235

feeding injury by A. saratogensis in the rated area, compare the percentage of ground in the 

stand covered by sweet fern to the percentage of ground covered by other host plants using 

Figure 3. Stands can be classified as having light, moderate or heavy potential injury, which 

are described below:

Potential injury rating Extent of potential injury

Light No visible symptoms of feeding other than scars beneath bark.

Moderate Some stunting and deformation, with occasional flagging.

Heavy Most pines stunted and deformed; many with flagging, topkill, or dead.

Stands can be rated before planting or after establishment. Reduce or remove the alternate 

host plants in stands rated as moderate or heavy as soon as possible before populations of A. 

saratogensis reach outbreak levels. Once outbreaks occur, damaging populations of A. saratogensis may persist for up to 10 years. Densely planted, vigorous stands of red pine should 

be less susceptible to A. saratogensis damage as the closing canopy will produce too much 

shade for most of the alternate host plants.

Note

This risk-rating method was developed for use in the Great Lakes region of North America 

and may not be applicable in other areas. Use with caution until validated in other regions.

Figure 3. Spittlebug risk-rating triangle showing regions of light (A), moderate (B), and heavy 

(C) risk. To determine potential injury level, plot the percentage of ground occupied by sweet-fern 

against the percentage of ground occupied by other suitable hosts. (Modified from the original figure 

in the publication.)

Percent Other Host Plants

Percent Sweet-fern

0 10 20 30 40 50 60 70 80 90 

100

A

B

C

0

10

20

30

40

50

60

70

80

90

100
236

Table 1. Spittlebug nymphal hosts and non-hosts commonly found in Lake States red pine 

plantations.

Vegetational group Common name Scientific name

Primary host Sweet fern Comptonia peregrina (L.) Coult.

Secondary hosts1 Blackberry, raspberry Rubus spp.

Orange hawkweed Hieracium aurantiacum L.

Strawberry Fragaria virginiana Dus.

Bracken fern Pteridium aquilinum (L.) Kuhn

Blueberry Vaccinium spp.

Sand cherry Prunus pumila L.

Goldenrod Solidago spp.

Sheep sorrel Rumex acetosella L.

Cinquefoil Potentilla spp.

Wintergreen Gaultheria procumbens L.

Sumac Rhus spp.

Non-hosts Grasses Gramineae

Sedges Carex spp.

Mosses Lycopodium spp.

Lichens Cladonia spp.

1

This group contains perhaps 200 species of plants that include herbs, ferns, woody shrubs, and young 

broadleaf trees.
237

Obscure Scale

Melanaspis obscura (Comstock)

Hemiptera: Diaspidae

Potter, D. A.; Jensen, M. P.; Gordon, F. C. 1989. Phenology and degree-day relationships of 

the obscure scale (Homoptera: Diaspididae) and associated parasites on pin oak in Kentucky. Journal of Economic Entomology 82: 551-555.

Objective

To determine the first hatch of M. obscura crawlers on pin oak using degree-day accumulation.

Abstract

Obscure scale, Melanaspis obscura (Comstock), is a native armored scale that attacks oaks, 

Quercus spp., maples, Acer spp., hickories, Carya spp., and other hardwoods in the eastern 

USA. Stressed trees, such as those in the urban setting, are more susceptible to attack by M. 

obscura. Infestations may result in branch dieback and a weakened condition, but infested 

trees rarely die from M. obscura alone.

When necessary, insecticide sprays can be applied against the overwintering stage or the 

newly-eclosed crawlers of M. obscura. Land managers can forecast the eclosion of M. 

obscura crawlers on pin oak, Quercus palustris Muenchhausen, used as street trees in Kentucky, using degree-day accumulation. Crawlers begin hatching at a mean accumulation of 

1,521 ±22 degree-days (DD) oC, which corresponds to late June-early July in northern Kentucky. Chemical applications applied during this time period should be effective against the 

crawler stage. 

Sampling Procedure

Begin monitoring degree-day accumulations from 1 January using a base threshold of 

4.44°C. Consider applying insecticides against M. obscura crawlers after 1,521 ± 22DD 

have accumulated.

Notes

While the mean degree-day accumulation of 1,521 ± 22DD gave a close prediction of crawler 

hatch in other states, this model should be used with caution outside of northern Kentucky 

until it has been validated with local temperature data. 
238

The peak activity period of the primary parasites of M. obscura is also in July. Application of 

chemical controls against M. obscura crawlers will have a negative impact on these parasites. 

Consider reserving insecticide controls for trees in stressed conditions, but refraining from 

treating otherwise healthy trees in order to conserve natural enemies. 
239

Birch Aphids

Betulaphis brevipilosa Börner

Callipterinella calliptera (Hartig)

Euceraphis betulae (Koch)

Hemiptera: Drepanosiphidae

Hajek, A. E.; Dahlsten, D. L. 1988. Distribution and dynamics of aphid (Homoptera: Drepanosiphidae) populations on Betula pendula in northern California. Hilgardia 56: 1-33.

Objective

To develop a collective aesthetic injury level for the aphids B. brevipilosa, C. calliptera, and 

E. betulae on European white birch, Betula pendula, in northern California.

Abstract

The aphids Betulaphis brevipilosa Börner, Callipterinella calliptera (Hartig), and Euceraphis 

betulae (Koch) feed on European white birch, Betula pendula Roth., which is often planted 

as an ornamental tree in North America. Large populations of these aphids produce copious 

amounts of honeydew that supports the growth of sooty mold, and attacked leaves may senesce prematurely. Homeowners who find the honeydew and mold unsightly may use chemical applications as preventive treatments to avoid this problem.

A collective aesthetic injury level was calculated for these aphids on B. pendula within the 

urban landscape in northern California. The authors estimated that 40 aphids per 100 cm 

of branch terminal or 88 aphids per 100 leaves warranted control measures if homeowners 

considered feeding damage by the aphids to be unsightly. Sample sizes necessary to estimate 

population density were determined using the coefficients of Taylor’s Power Law. Sampling 

effort depends on aphid density and the desired level of reliability in estimating aphid density. Overall, the aggregated distribution of these aphids requires large numbers of samples 

from each tree even if only a low level of reliability is desired. Higher aphid densities require smaller sample sizes and less time is spent sampling each tree, thus sampling cost is 

also lower than if aphid densities were low. Sampling in the spring, when aphid populations 

were high, took <2 minutes per branch while sampling in the summer, after populations fell, 

required <1.5 minutes per branch. Few aphids were found in the upper third of the canopy, 

therefore sampling effort should concentrate on the lower two-thirds of the tree.

Sampling Procedure

The sampling unit consists of all the leaves found on a new long terminal shoot, plus the 

leaves on any short shoots occurring along the previous year’s twig growth of a length equal 
240

to the terminal shoot. Examine leaves and shoots closely for immature and adult aphids. Record the number of aphids found on leaves and on the branch separately. Average the number of aphids per 100 cm branch terminal or per 100 leaves. If feeding damage, honeydew 

production, or sooty mold accumulation is undesirable, consider insecticide treatment when 

aphid populations reach 40 aphids per 100 cm of branch terminal or 88 aphids per 100 leaves. 

To estimate aphid densities on groups of trees, randomly sample 5 branches per tree throughout the lower two-thirds of the canopy when aphid populations are low (approximately 5 

aphids per 100 leaves) and 3 branches per tree when aphid populations are high (approximately 40 aphids per 100 leaves). Refer to Fig. 1 for the number of trees to sample to estimate aphid populations at either low or high aphid densities using a 70% confidence limit. 

Sampling costs in person-hours are illustrated in Fig. 2 for both low and high aphid densities 

with a 70% confidence limit.

Use Table 5 to determine the total number of branch samples necessary to estimate aphid 

densities on individual trees. For example, 174 branch terminals should be taken from each 

tree to estimate aphid density when aphids are estimated to be 10 per 100 cm branch length 

and 30% reliability is desired (Table 5). 

Note

These recommendations are based on data collected in northern California and may not apply 

to other aphid species on different host birch species in other regions. Use these recommendations with caution until validated in other locations.
241

Fig. 1. Numbers of trees to sample for estimation of aphid populations on Betula pendula

with varying levels of reliability and numbers of branches per tree (70 percent confidence 

limits). (A) 40 aphids/100 leaves; (B) 5 aphids/100 leaves.
242

Fig. 2. Sampling costs (in person-hours) to optimally estimate aphid population densities on 

Betula pendula for varying levels of reliability (70 percent confidence limits).
243

Table 5. Sample size in number of branches for estimation of aphid populations on individual Betula pendula trees at varying levels of reliability and aphid population densities.

Reliability with 70% confidence intervals

(± % of the mean)

Mean aphid

density 20% 30% 40% 50% 60%

(/100 cm) (number of branches)

10 391 174 98 63 43

20 320 142 80 51 36

30 285 126 71 46 32

40 262 116 65 42 29

50 245 109 61 40 27

Figures 1 and 2 and Table 5 reproduced with permission from Regents of the University 

of California (copyright 1988), granted March 26, 2009.
244

Sycamore Lace Bug

Corythucha ciliata (Say)

Hemiptera: Tingidae

Horn, K. F.; Farrier, M. H.; Wright, C. G.; Nelson, L. A. 1983. A sampling method for estimating egg and first-instar densities of the sycamore lace bug, Corythucha ciliata (Say). 

Journal of the Georgia Entomological Society 18: 37-49.

Objective

To develop fixed precision estimation plans for eggs and first-instar nymphs of C. ciliata on 

individual trees using Taylor’s power law.

Abstract

The sycamore lace bug is a minor pest of sycamore, Platanus occidentalis L. In 1983, the 

importance of C. ciliata in Europe, where it is an exotic pest, was increasing. A sequential 

estimation plan was devised for eggs and first-instar nymphs to aid in basic research on this 

insect.

Both plans were based on Taylor’s power law. Log variance was related positively to C. ciliata eggs (log variance = log 19.11 + 1.393log mean; r2

 = 0.94) and first-instar nymphs (log 

variance = log 7.831 + 1.594log mean; r2 = 0.92). This information was used to develop sequential estimation plans for eggs and first-instar nymphs at the 0.20 and 0.25 levels of fixed 

precision. Control measures were recommended if densities of C. ciliata exceeded 2 eggs/

cm2

. 

Sampling Procedure

Select sycamore trees at random. Sample an equal number of similar-sized leaves from each 

cardinal direction from the lower (≤3.5 m) part of the canopy. The third to fifth leaves from 

the terminal leaf were used in this study. On the underside of each leaf, count the number of 

eggs or first instars per leaf. Average the densities among all leaves sampled from one tree. 

If the leaf appears to have more than 100 eggs on it, count the number of eggs on one half of 

the leaf divided by the midvein and then double the tally for the estimate of that leaf. Stop 

sampling leaves when the mean number of eggs or first instars per leaf crosses the desired 

level of fixed precision at either of the t-values (probability that the mean is within the level 

of fixed precision) (see Figs. 2 & 3 for eggs or nymphs, respectively). Trees are classified 

as lightly or heavily infested if the density of eggs is <1.0 or >2.0 eggs per cm2

 of leaf area, 

respectively. However, leaf area must also be sampled to use this threshold. Allow at least 2 

weeks to pass after sampling eggs before sampling first instars to allow all eggs to hatch.
245

Notes

The authors did not specify infestation levels for first instars; use the infestation levels given 

for eggs with caution. This sampling plan was developed from samples on relatively few 

trees in one location. Thus, variation in mean and variance among different populations of 

C. ciliata was not addressed in this study. Please use this plan with caution if sampling lace 

bugs in locations other than Raleigh, NC.

Fig. 2. Number of leaves needed to estimate the mean Corythucha ciliata egg density within a tree with a given 

level of accuracy and reliability (A) D = .20 and (B) D = .25. Dashed and solid lines represent the critical 

t-statistics at the 0.1 and 0.2 probability levels (two-tailed), respectively.
246

Fig. 3. Number of leaves needed to estimate the mean Corythucha ciliata first-instar density within a tree with 

a given level of accuracy and reliability (A) D = .20 and (B) D = .25. Dotted, dashed, and solid lines represent 

the critical t-statistics at the 0.1, 0.2, and 0.5 probability levels (two-tailed), respectively

Figures 2 and 3 reproduced with permission from the Journal of Entomological Science, 

granted April 2, 2009.
247

seed and cone insects
248
249

Spiral Spruce Cone Borer

Strobilomyia neanthracina (Michelsen)

Spruce Seed Moth

Cydia strobilella (L.) 

Diptera: Anthomyiidae

Ruth, D. S.; Miller, G. E.; Sutherland, J. R. 1982. A guide to common insect pests & diseases 

in spruce seed orchards in British Columbia. Inf. Rep. BC-X-231. Environment Canada, 

Canadian Forestry Service, Pacific Forest Research Centre; 28 p.

Objective

To provide a rough treatment threshold for spruce conelets, Picea spp., infested by C. strobilella and S. neanthracina.

Abstract

Spruce seedmoth, Cydia strobilella (L.), is an important pest of spruce (Picea spp.) seeds. 

Larvae feed inside seeds where the damage is cryptic. Spruce cone maggot, Strobilomyia 

neanthracina Michelsen, is a major pest of spruce cones and seeds. Larvae tunnel within 

cones in a characteristic spiral manner, consuming seeds developing at the base of scales. 

Primary hosts of both pest species include white spruce, Picea glauca (Moench) Voss, Engelmann spruce, Picea engelmannii Parry, and other Picea spp. Both pest species can cause 

substantial seed loss in seed orchards in Canada and USA. 

Random sampling of 5–10% of the cone-bearing spruces in an orchard can provide a rough 

estimate of seed loss by C. strobilella and S. neanthracina. Ten conelets should be taken 

from each sampled tree and examined visually for eggs. A seed loss of 10–20% can be 

expected at harvest if eggs of either species are found on two conelets from each tree, thus 

control measures are warranted if more than one cone per tree is infested. 

Sampling Procedure 

Immediately after pollination in the spring, randomly select 5–10% of the cone-bearing 

spruces in an orchard. Collect 10 conelets throughout the canopy of each selected tree. Using a hand lens, examine each conelet for the presence of C. strobilella and S. neanthracina

eggs. If two conelets from each tree are found to have eggs of either pest species on them, 

a seed loss of 10–20% can be expected at harvest. Managers should consider control measures if more than 1 conelet per tree is infested and the majority of conelets in the orchard are 

closed and are turning downwards. Seed loss may have already occurred by the time conelets are pendant
250

Western Conifer Seed Bug

Leptoglossus occidentalis Heidemann

Hemiptera: Coreidae

Blatt S. E.; Borden, J. H. 1996. Distribution and impact of Leptoglossus occidentalis Heidemann (Hemiptera: Coreidae) in seed orchards in British Columbia. Canadian Entomologist 

128: 1065-1076.

Objective

To determine the distribution of L. occidentalis in coastal Douglas-fir and lodgepole pine 

seed orchards.

Abstract

Western conifer seed bug, Leptoglossus occidentalis Heidemann, feeds on seeds developing 

within the cones of coastal Douglas-fir, Pseudotsuga menziesii (Mirb.) Franco, and several 

species of pine (Pinus spp.). It is considered a serious pest in seed orchards in the USA 

and British Columbia. Studies on the distribution of L. occidentalis in coastal Douglas-fir 

seed orchards in British Columbia indicated that this species has a patchy distribution as the 

nymphs feed in aggregations. Thus, whole tree counts may be more appropriate than sampling a set number of branches when population densities are low. This species was not observed on trees without cones, suggesting that coneless trees can be excluded from sampling. 

X-ray analysis of seeds indicated that L. occidentalis was responsible for <5% and ≈14% 

seed loss in coastal Douglas-fir and lodgepole pine, Pinus contorta var. latifolia Engelm., 

respectively. Seed loss was not related to L. occidentalis density on either host species. The 

authors concluded that current populations of L. occidentalis in seed orchards in British 

Columbia are not large enough to warrant control measures on either coastal Douglas-fir or 

lodgepole pine. 

Sampling Procedure

Tree sampling: Randomly select trees bearing cones in mid-July. Visually select three conebearing branches from each tree from a distance, then examine each selected branch for adult 

and nymphs of L. occidentalis. Use a ladder or bucket truck as needed. Given the aggregation behavior of L. occidentalis nymphs, whole tree counts are probably more appropriate for 

nymphs than just sampling three branches from a tree.

Seed sampling: Randomly select 25 cones from trees at harvest. Dry cones in paper bags, 

then extract and winnow seeds before x-ray analysis. Examine radiographs to categorize 
251

seeds as full, empty, or partially full. Douglas-fir seeds may also be categorized as infested 

by the Douglas-fir seed chalcid, Megastigmus spermotrophus Wachtl.

The number of L. occidentalis causing 5, 10, 15, and 20% seed loss over a 120-day feeding 

period is given in Table 2. Populations of L. occidentalis in British Columbia are probably 

not large enough to exceed the acceptable seed loss of <5%.

Reference

Hanson, P. D. 1984. Comparison of damage to Douglas-fir [Pseudotsuga menziesii (Mirb.) 

Franco] seed, by each life stage of Leptoglossus occidentalis Heidemann (Hemiptera: Coreidae). Vancouver: University of British Columbia; B.Sc. thesis. 28 p.
252Table 2. Calculated numbers of Leptoglossus occidentalis required to cause 5–20% damage to coastal Douglas-fir and lodgepole pine seed based on Hansen’s (1984) feeding rate of one seed bug per day, and extended over an estimated 120 days of favorable 

weather for feeding from 1 May to 31 August in British Columbia. Estimates of cones per tree and filled seed per cone based on data supplied by D. Reid and B. Barber, B.C. Forest Service, Saanichton and Victoria, respectively, who obtained records from the Tree Seed Centre, B.C. Forest Service, Surrey, B.C.

Species Source of seed Cones per tree

Filled seed

per cone

Filled seed

produced

Percentage 

damage

Seed

damaged

Insects required 

per tree

Douglas-fir Seed orchard 3,000 40 120,000 5 6,000 50

10 12,000 10015 18,000 15020 24,000 200

Wild stand 2,800 14 39,200 5 1,960 17

10 3,920 3315 5,880 4920 7,840 66

Lodgepole pine Seed orchard 2,000 15 30,000 5 1,500 13

10 3,000 2515 4,500 3820 6,000 50

Wild stand 8,300 20 166,600 5 8,300 70

10 16,600 13915 24,900 20820 33,200 277

Table 2 modified and reproduced with the permission of the authors, granted April 2, 2009.
253

Western Conifer Seed Bug

Leptoglossus occidentalis Heidemann

Hemiptera: Coreidae

Bates, S. L.; Lait, C. G.; Borden, J. H.; Kermode, A. R. 2002. Measuring the impact of Leptoglossus occidentalis (Heteroptera: Coreidae) on seed production in lodgepole pine using an 

antibody-based assay. Journal of Economic Entomology 95: 770-777.

Objective

To improve the detection of damage caused by L. occidentalis in harvested lodgepole pine 

seed using an immunoassay.

Abstract

Western conifer seed bug, Leptoglossus occidentalis Heidemann, feeds on developing seeds 

within the cones of lodgepole pine, Pinus contorta var. latifolia Engelm. This seed bug 

occurs throughout western North America but appears to be spreading into eastern Canada 

and the USA. Adult male and female L. occidentalis can damage 1.4 and 2.0 seeds per day, 

respectively, in late cone development. Seed loss due to L. occidentalis can be difficult to 

calculate, as seed damaged by pest feeding cannot be distinguished from aborted seed at 

harvest unless examined using x-rays. Seed loss estimates are based typically on exclusion 

studies or caged feeding trials. A new method has been developed using an antibody marker 

for a salivary protein deposited by L. occidentalis while feeding on seeds (Lait et al. 2001). 

Managers interested in using this immunoassay are strongly encouraged to consult the original publication for detailed information regarding this technique. This method accurately 

detects seeds damaged by L. occidentalis, but traditional radiograph techniques are necessary 

to estimate the percentage of empty seeds aborted due to other causes.

Sampling Procedure

Collect cones at harvest and store at ≈18°C until processing. Boil cones in water for ≈90 s to 

remove the resin seal, then bake at 50°C for 8 h. Remove seeds from cones by shaking them 

vigorously in a container for 90 s. Seeds not dislodged during this procedure are classified as 

being fused to the cone scales.

Grind individual seeds in 150 μl of a buffer made of 62.5 mM Tris-HCL (pH 6.8), 2% sodium dodecyl sulfate (wt:vol), and 10% glycerol (vol:vol). Load 15 μl of each seed sample 

on a 15% acrylamide resolving gel and separate the proteins using sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) under reducing conditions with 2 μl 
254

β-mercaptoethanol per sample. Transfer separated proteins onto nitrocellulose with a Western blot. Block overnight in Tris-buffered saline (TBS) made with 5% nonfat milk powder. 

Incubate Western blots for 2 h at room temperature with affinity-adsorbed primary polyclonal 

antibody (1:5000) to L. occidentalis salivary protein (Lait et al. 2001). Positive binding of 

the antibody can be demonstrated by using a secondary antibody conjugated to alkaline phosphatase (1:10000) with 5-bromo-4-chloro-3-indoyly phosphate and nitro blue tetrazolium, 

which react together and form a colored product. Seed samples with this colored product are 

classified as damaged by L. occidentalis feeding. Seeds fused to cone scales should also be 

considered as being damaged by L. occidentalis feeding from early in the season. Managers 

must use their experience and the estimated size and value of the expected seed crop to decide if the seed loss determined by this method warrants control measures for L. occidentalis

the following year.

Notes

The authors did not suggest a minimum number of cones to sample, but they should be collected from a sufficient number of randomly selected trees to adequately assess feeding damage throughout the orchard.

The authors noted that some seeds exposed to L. occidentalis early during seed development 

may be empty when harvested but not test positive for L. occidentalis feeding. Also, feeding by L. occidentalis on cones early in the season increased the number of seeds fused to the 

cone scales. The fused seeds, remaining in the cone after the extraction process, are often 

overlooked as feeding injury on the seed crop. The authors noted that not all fused seeds, 

when removed from the cones and tested with the immunoassay, produced the colored product indicating L. occidentalis feeding damage despite their association with L. occidentalis. 

In both cases, these false-negative results may be due to assimilation of the salivary proteins 

by the developing ovule or degradation of the proteins over time before harvest. For this 

reason, the immunoassay is not wholly accurate in detecting L. occidentalis damage that may 

occur early in the season.

Reference

Lait, C. G.; Bates, S. L.; Kermode, A. R.; Morrissette, K. K.; Borden, J. H. 2001. Specific 

biochemical marker-based techniques for the identification of damage to Douglas-fir seed 

resulting from feeding by the western conifer seed bug, Leptoglossus occidentalis Heidemann (Hemiptera: Coreidae). Insect Biochemistry and Molecular Biology 31: 739–746.
255

Western Conifer Seed Bug

Leptoglossus occidentalis Heidemann

Hemiptera: Coreidae

Bates, S. L.; Borden, J. H. 2005. Life table for Leptoglossus occidentalis Heidemann (Heteroptera: Coreidae) and prediction of damage in lodgepole pine seed orchards. Agricultural 

and Forest Entomology 7: 145-151.

Objective

To predict damage caused by L. occidentalis on harvested seed yield in lodgepole pine seed 

orchards in British Columbia.

Abstract

Western conifer seed bug, Leptoglossus occidentalis Heidemann, feeds on developing seeds 

within the cones of lodgepole pine, Pinus contorta var. latifolia Engelm. Feeding damage by 

L. occidentalis represents a significant economic loss for seed orchards in British Columbia 

as lodgepole pine seed is valued at $1,000–5,000 per kg (Strong et al. 2001). In this study 

the authors estimated that one L. occidentalis per tree early in the season will result in approximately 310 seeds lost to feeding damage at harvest. This damage reflects feeding by 

both a founding bug and the expected offspring. In the absence of any predation or parasitism, approximately 434 seeds per tree lost to feeding damage can be expected at harvest. 

This damage prediction is based on the population dynamics of the bug over the season as 

well as feeding studies conducted by Bates et al. (2002). Adult male and female L. occidentalis can damage 1.4 and 2.0 seeds per day, respectively (Bates et al. 2002). 

Sampling Procedure

Early in the season when nymphs are present, beat lodgepole pines thoroughly using a beat 

stick and a tray or a drop cloth. Sample as much of the canopy as possible. Examine the 

tray or drop cloth and count the number of L. occidentalis nymphs and adults present. The 

authors did not suggest a minimum number of trees to sample, but the density of L. occidentalis within an orchard should be averaged over a sufficient number of randomly selected 

trees. Managers must use their experience and the estimated size and value of the expected 

seed crop to decide if the expected seed loss produced by 0.5 to 2.5 bugs per tree early in the 

season warrants control measures (see Fig. 1 in original publication).

The authors recommend sampling throughout the season as adult populations, and associated seed losses, may rise quickly late in the season. Sample for L. occidentalis nymphs and 

adults as described above, but count male and female adults separately. The damage predic-
256

tion for seed loss due to adult feeding late in the season is based on the numbers of adults 

present and days to harvest, but not the effect of potential offspring. Use Table 6 (refer to 

original publication) to predict the number of damaged seeds per tree based on the developmental stage, sex, density of bugs per tree, and the number of days remaining to harvest. 

Again, managers must use their experience and the estimated size and value of the expected 

seed crop to decide if the predicted seed loss warrants control measures before harvest.

Notes

Refer to the original publication for Figure 1 and Table 6, which could not be reprinted here 

due to the immoderate charges requested by the publisher for copyright clearance.

The authors noted that first instar L. occidentalis were difficult to sample as they tended to 

be tossed into the air rather than dropping onto the tray or drop cloth when trees were beaten. 

L. occidentalis occurs throughout western North America but appears to be spreading into 

eastern Canada and the U.S. Damage predictions established for L. occidentalis in western 

Canada may not be applicable for L. occidentalis in its newly expanded range in eastern 

North America.

References

# Bates, S. L.; Lait, C. G.; Borden, J. H.; Kermode, A. R. 2002. Measuring the impact of Leptoglossus occidentalis (Heteroptera: Coreidae) on seed production in lodgepole pine using 

an antibody-based assay. Journal of Economic Entomology 95: 770-777.

Strong, W. B.; Bates, S. L.; Stoehr, M. U. 2001. Feeding by Leptoglossus occidentalis

(Hemiptera: Coreidae) reduces seed set in lodgepole pine (Pinaceae). Canadian Entomologist 133: 857-865.
257

Western Conifer Seed Bug

Barbara colfaxiana (Kearfott), Lepidoptera: Tortricidae

Contarinia oregonensis Foote, Diptera: Cecidomyiidae

Dioryctria abietivorella (Grote), Lepidoptera: Pyralidae

Lepesoma lecontei (Casey), Coleoptera: Circulionidae

Megastimus spemotrophus (Wachtl), Hymenoptera: Torymidae

Dombrosky, S. A.; Schowalter, T. D. 1988. Inventory monitoring for estimating impact of 

insects on seed production in a Douglas-fir seed orchard in western Oregon. Journal of 

Economic Entomology 81: 281-285.

Objective

To develop a fixed precision enumerative sampling plan that estimates infestation levels of 

multiple species of seed and cone insects attacking cones and seeds of coastal Douglas-fir.

Abstract

Genetically improved Douglas-fir, Pseudotsuga menziesii (Mirb.) Franco, is commonly 

grown in orchards in the Pacific Northwest to aid reforestation efforts in the coastal region. 

The value of harvested seed ranged from $650-2,200/kg in the 1980’s, and any damaged seed 

is considered an economic loss. A number of important insect pests have been reported from 

Douglas-fir cones, feeding on the cone and developing seeds within the cone.

A study conducted in western Oregon examined the distribution of Douglas-fir cones and 

insects attacking the cones, as stratified within crowns and among trees in a seed orchard. 

The number of trees and the number of cones per tree or the number of cones per crown level 

(bottom, mid, and upper) was determined for sampling multiple cone pests at the 0.10 and 

0.20 standard errors (SE) of the mean. In general, 12 cones collected randomly throughout 

the canopies of each of 100 trees should be adequate to estimate seed losses due to biotic 

or abiotic factors with a standard error of the mean of 0.20, but stratified sampling among 

crown levels is recommended for more precise estimates of potential cone and seed yields. 

Sampling large numbers of trees is necessary due to the high level of variation among trees. 

Orchard managers should select a sampling plan that balances the desired level of precision 

with an acceptable cost of sampling.

Sampling Procedure 

For non-stratified sampling of biotic and abiotic factors contributing to cone and seed losses, 

collect 12 cones randomly throughout the canopy of each of 100 trees selected at random in a 

seed orchard. This should be sufficient to estimate seed losses at the 0.20 standard error level 
258

of precision. Examine cones and seeds to determine mortality, total seeds per cone, the number of healthy seeds, and seed loss due to insects, frost, unexplained failure to develop, etc.

Sample immature cones at the flowering stage in April or the pendant stage in June for the 

flightless weevil Lepesoma lecontei (Casey) (Coleoptera: Curculionidae). To sample L. 

lecontei at the 0.10 standard error of the mean, examine 584 cones throughout the crown 

from each of 781 trees (Table 4). Alternatively, partition the live crown of each of the 781 

trees into thirds by counting the number of whorls present on each tree and dividing by three 

(Miller 1986a, b). Sample 92 cones from the bottom crown, 390 cones from the mid-crown, 

or 1,111 cones from the top crown of each tree.

To sample L. lecontei at the 0.20 standard error of the mean, examine 146 cones throughout 

the crown from each of 195 trees (Table 4). Alternatively, partition the live crown of each of 

the 195 trees into thirds by counting the number of whorls present on each tree and dividing 

by three (Miller 1986a, b). Sample 23 cones from the bottom crown, 98 cones from the midcrown, or 278 cones from the top crown of each tree.

Sample mature cones in early September for the following insects that damage seeds:

Douglas-fir cone gall midge Contarinia oregonensis Foote Diptera: Cecidomyiidae

Douglas-fir cone moth Barbara colfaxiana (Kearfot) Lepidoptera: Tortricidae

Douglas-fir seed chalcid Megastimus spermotrophus (Wachtl) Hymenoptera: Torymidae

Fir coneworm Dioryctria abietivorella (Grote) Lepidoptera: Pyralidae

Use Table 4 to determine how many trees, cones per tree, or cones per crown level should 

be sampled for each of these pests. For example, to sample M. spermotrophus at the 0.10 

standard error of the mean, examine 49 cones throughout the crown from each of 620 trees. 

Alternatively, partition the live crown of each tree into thirds by counting the number of 

whorls present on each tree and dividing by three (Miller 1986a, b). Sample 15 cones from 

either the bottom or mid-crown of each tree, or 25 cones from the top crown of each tree. To 

sample M. spermotrophus at the 0.20 standard error of the mean, examine 12 cones throughout the crown from each of 158 trees. Alternatively, partition the live crown of each tree into 

thirds by counting the number of whorls present on each tree and dividing by three (Miller 

1986a, b). Sample 4 cones from either the bottom or mid-crown of each tree, or 6 cones from 

the top crown of each tree. 

Examine each cone for characteristic damage produced by insects attacking seeds and determine the percentage of healthy seeds or seeds damaged by each insect pest. When sampling M. spermotrophus, dissect each cone to remove the scales. X-ray the seeds that appear 

healthy to detect infestation by M. spermotrophus or other internal damage. 

Notes

Table 4 was generated from a sample of ten trees at one site in one year. This information 
259

should be used with caution until additional data can be collected to verify or adjust the numbers. Additional data collected from other seed orchards throughout the Pacific Northwest 

will greatly improve the predictive ability of this inventory monitoring system. 

References

* Miller, G.E. 1986a. Distribution of Contarinia oregonensis Foote (Diptera: Cecidomyiidae) eggs in Douglas-fir seed orchards and a method for estimating egg density. Canadian 

Entomologist 118: 1291-1295.

* Miller, G.E. 1986b. Damage prediction for Contarinia oregonensis Foote (Diptera: Cecidomyiidae) in Douglas-fir seed orchards. Canadian Entomologist 118: 1297-1306.

Table 4. Minimum number of trees and cones required for seed loss estimates within a predetermined standard error (SE), based on data from 10 study trees at the Beaver Creek Seed 

Orchard, 1984.

Cones/tree level

Factor Trees

(n)

Cones/tree

(n)

Bottom

(n)

Middle

(n)

Top

(n)

SE = 0.10

Healthy seeds 565 52 15 22 28

Undeveloped seeds 73 2 1 1 2

Gall midge 312 51 59 13 19

Seed chalcid 620 49 15 15 25

Cone moth 605 895 52 945 627

Coneworm 1,020 706 368 468 523

Other 207 148 33 124 75

Weevil 781 584 92 390 1,111

Other cone mortality 379 157 133 198 758

SE = 0.20

Healthy seeds 144 13 4 6 6

Undeveloped seeds 21 0.5 0.25 0.25 0.5

Gall midge 79 13 15 3 5

Seed chalcid 158 12 4 4 6

Cone moth 151 224 13 236 157

Coneworm 255 177 92 117 131

Other 52 37 8 31 19

Weevil 195 146 23 98 278

Other cone mortality 95 39 33 50 190

Table 4 reproduced with permission from the Journal of Economic Entomology, granted April 

2, 2009.
260

Multiple Seed and Cone Insects

Fogal, W. H.; Larocque, G. 1992. Development of flowers, cones, and seeds in relation to 

insect damage in two white spruce communities. Forest Ecology and Management 47: 

335-348.

Objective

To determine the optimal time to sample seed and cone pests of white spruce, Picea glauca, 

based on the temporal relationship between seed and cone development and the occurrence 

of pest damage.

Abstract

Seed- and cone-feeding insects of white spruce, Picea glauca (Moench) Voss, can cause substantial seed loss in seed orchards. Trees are frequently sprayed for seed and cone pests after 

the upright cones turn to a pendant position to avoid interfering with pollination while cones 

are upright (Fogal and Plowman 1988). However, research on the temporal relationship between seed and cone development of white spruce and the occurrence of damage by seed and 

cone insect pests indicated that spruce budworm, Choristoneura fumiferana (Clem.), spruce 

coneworm, Dioryctria reniculelloides Mutuura and Munroe, and spruce seedmoth, Cydia 

strobilella (L.), damaged some cones before they assumed the pendant position. Waiting 

until cones have turned to a pendant position before applying insecticides may be too late to 

prevent some losses by these pests. 

Depending on the life cycle of the species of pest to be controlled, insecticides should be applied before or soon after pollination to limit seed loss. Insecticides should be applied before 

pollination against C. fumiferana and most likely D. reniculelloides as these larvae feed on 

microstrobili and megastrobili before fertilization occurs. Systemic insecticides against C. 

strobilella and other insects that oviposit directly into seed cones after pollination should 

be applied before or soon after pollination to allow the translocation of the material into the 

cones. However, avoid any canopy sprays that interfere with pollination. 

The efficacy of insecticide applications can be assessed by sampling insect damage. Sample 

developing cones no earlier than mid-July to maximize sampling effort. Waiting until midJuly helps avoid underestimating seed losses due to late-season insect pests or overestimating 

seed yield due to seed abortion or cones dropping prematurely later in the season. Managers 

may consider canceling the harvest if heavy damage to cones and seeds is found (Dobbs et al.

1976). Damage detected at this sampling date cannot be abated by the use of insecticide applications this late in the season, but large populations of seed and cone insects in the samples 
261

may signal the need for insecticide application the following season to protect the next crop. 

Sampling Procedure 

Sample developing cones no earlier than mid-July for the following insects that damage 

seeds and cones:

Insect Type of feeding damage

Spruce budworm,

Choristoneura fumiferana (Clem.) Curled, distorted cones

Spruce cone-axis midge,

Dasineura rachiphaga Tripp

One or more larvae in silken cocoons in cone-axis 

gallery

Spruce cone maggot,

Strobilomyia neanthracina Michelsen

Reddish-brown tunnels filled with resin around cone 

axis.

Spruce coneworm,

Dioryctria reniculelloides Mutuura and Munroe Cone-axis gallery filled with frass, silk

Spruce seed chalcid,

Megastigmus piceae Rohwer Larvae in seed

Spruce seed midge,

Mayetiola carpophaga (Tripp) Larvae in seed

Spruce seedmoth,

Cydia stobilella (L.)

Seeds filled with frass; one or more larvae in coneaxis gallery

Cones infested by C. fumiferana and D. reniculelloides can be distinguished by the characteristic damage patterns made by the larvae. Examine longitudinal sections of cones and seeds 

under a dissecting microscope to identify the other insect species present. Larvae in seed 

were recorded as seed-inhabiting insects in the study and were not identified to species.

Sampling after mid-July will ensure that insects damaging cones and seeds late in the season 

are included in the assessments and that a more accurate assessment of seed loss is noted. 

For example, trees may abscise cones attacked by C. fumiferana, S. neanthracina, or C. 

strobilella in mid-season so that late season assessments may underestimate damage by these 

pests. However, damage by D. rachiphaga and seed pests such as M. carpophaga and Megastigmus piceae is less evident early in the season and may be overlooked if assessments are 

made too early. Sampling seed and cone pests after mid-July offers a good compromise in 

terms of maximizing sampling efforts, but multiple samples made over the season are preferable to a single sample. 

References

Dobbs, R. C.; Edwards, D. G. W.; Konishi, J.; Wallinger, D. 1976. Guidelines to collecting cones of 

B.C. conifers. Joint Rep. No. 3. Victoria, B.C.: Canadian Forest Service, British Columbia Forest 

Service; 98 p.

Fogal, W. H.; Plowman, V. C. 1989. Systemic insecticides for protecting northern spruce and pine 

seed trees. Inf. Rep. PI-X-92. Chalk River, Ont.: Natural Resources Canada, Canadian Forest Service, Petawawa National Forestry Institute; 17 pp. 
262

Multiple Seed and Cone Insects

Kozak, A. 1964. Sequential sampling for improving cone collection and studying damage by 

cone and seed insects in Douglas fir. Forestry Chronicle 40: 210-218.

Objectives

To develop a sequential sampling plan for cone and seed insects on individual Douglas-fir 

trees; to develop a sequential sampling plan for determining the percentage of Douglas-fir 

cones with adequate filled seeds for collection.

Abstract

Seed- and cone-feeding insects of Douglas-fir [Pseudotsuga menziesii (Mirb.) Franco] can 

cause substantial seed loss in seed orchards. Common pests include Douglas-fir cone pyralid (Dioryctria abietella Denis & Schiffermüller), Douglas-fir seed chalcid [Megastigmus 

spermotrophus (Wachtl)], Douglas-fir cone gall midge (Contarinia oregonensis Foote) and 

Douglas-fir cone moth [Barbara colfaxiana (Kearfott)]. These insects were monitored on 

Douglas-fir cones in a study conducted in British Columbia and a sequential sampling plan 

was developed for these pests on individual trees. Testing of this plan showed that an individual tree could be classified in terms of filled seed as poor, medium, or good within 30 

minutes and usually by sampling <50 cones. The sampling plan is based on the collective 

damage produced by seed- and cone-feeding insects and does not require the identification of 

which species produced the damage, making it very useful to land managers without specialized training in entomology.

This technique is recommended for lightly to moderately infested trees. 

Sampling Procedure

To sample damaged cones from individual Douglas-fir trees: Randomly select trees for sampling 

in mid-August or when damage produced by common species of cone- and seed-feeding insects is 

visible to the naked eye. Randomly select cones throughout the canopy of selected trees if at all possible. Slice each cone in half longitudinally (Winjum and Johnson 1960) and count the number of 

filled and empty seeds found on one cut surface (one half of the cone). Refer to Table II to determine 

how many cones should be sampled based on the undamaged filled seed found on the cut surface of 

each cone. Continue sampling until the tree can be classified as poor, medium, or good.

The relationship between the number of undamaged filled seed on the cut surface of a cone (X) 

and the number of undamaged filled seed per cone (Y) can be expressed using the formula Y = 

3.04X – 0.33 (Kozak et al. 1963).
263

To sample damaged cones from a stand of Douglas-fir (not from individual trees): Randomly 

select trees for sampling in mid-August or when damage produced by common species coneand seed-feeding insects is visible to the naked eye. Randomly select four cones from a 

maximum of 50 trees. Slice each cone in half longitudinally and count the number of filled 

and empty seeds found on one cut surface (one half of the cone). In general, cones with at 

least four filled seeds on the cut surface of one half of the cone are considered satisfactory for 

economical seed extraction. Using the formula Y = 3.04X – 0.33 as described above, 35.2 L 

of cones (approximately 1,000 cones) should produce at least 11,830 seeds if at least 4 filled 

seeds are found on the cut surface of each cone. Land managers can use this crude estimate 

to judge whether a stand should be harvested for seed or not.

Notes

This sampling plan does not require differentiation among the insect species damaging seeds 

and cones. However, see the original publication for sequential graphs for sampling Douglas-fir cone gall midge and Douglas-fir seed chalcid separately. 

The original article also contains a procedure for determining the number of cones that 

should be sampled in order to classify a stand as having sufficient cone quality to justify seed 

extraction. Sampling heavily infested stands using this procedure may not be economical as, 

on average, more than 100 trees should be sampled before the classification can be made due 

to inter-tree variation. The effort required to classify a stand makes it unattractive for operational use and was not considered practical by the authors, thus it is not presented in this 

summary.

References

Kozak, A.; Sziklai, O.; Griffith, B. G.; Smith, J. H. G. 1963. Variation in cone and seed yield 

from young open-grown Douglas firs on the U.B.C. Research Forest. Res. Paper 57. Vancouver, BC: University of British Columbia, Faculty of Forestry; 8 p. 

Winjum, J. K.; Johnson, N. E. 1960. A modified knife cone cutter for Douglas fir seed studies. Journal of Forestry 58: 487-488.
264

Table 2. Sequential table for sampling undamaged filled seeds.

Cumulative Number of Undamaged Filled Seeds

No. of 

Cones 

Examined

Porr vs. Medium Medium vs. Good

Upper Limit 

of Poor

Lower Limit 

of Medium

Upper Limit 

of Medium

Lower Limit 

of Good

1 –

CONTINUE SAMPLING

– –

CONTINUE SAMPLING

32

2 – – – 38

3 – – – 43

4 1 – – 49

5 4 – – 54

6 6 – – 60

7 9 – – 65

8 11 – – 71

9 14 – – 76

10 16 – – 82

11 19 – – 87

12 21 – – 93

13 23 – – 98

14 26 – – 104

15 28 – – 109

16 21 – – 115

17 33 – – 120

18 36 – – 126

19 38 – – 131

20 41 – – 137

21 43 – – 142

22 45 – – 148

23 48 65 99 153

24 50 67 105 159

25 53 70 110 164

26 55 72 115 170

27 58 75 121 175

28 60 77 126 180

29 63 80 132 186

30 65 82 137 191

Table 2 reproduced with permission from the Forestry Chronicle, granted April 22, 

2009.
265

WOOD- AND BARK-BORING INSECTS
266
267

Bronze Birch Borer

Agrilus anxius Gory

Coleoptera: Buprestidae

Akers, R. C.; Nielsen, D. G. 1984. Predicting Agrilus anxius Gory (Coleoptera: Buprestidae) adult 

emergence by heat unit accumulation. Journal of Economic Entomology 77: 1459-1463. 

Objective

To develop a degree-day model of adult A. anxius emergence to improve the timing of treatment applications.

Abstract

Bronze birch borer, Agrilus anxius Gory, is a severe pest of white-barked birches, Betula

spp., in North America. Stressed trees in urban settings are particularly susceptible to A. 

anxius, but infestations also occur in forests. Larvae, known as flathead borers, tunnel into 

the trunks of trees and consume the cambium and phloem before emerging as adults from Dshaped exit holes. Infestations result in the girdling of the cambium and symptoms include 

a gradual progression of canopy thinning and branch dieback from the upper crown towards 

the base of the tree. Severely weakened trees die, but some may survive with reduced aesthetic value. 

Pheromone traps are not available for A. anxius or other buprestids. Management of buprestid species typically consists of insecticide sprays applied in the spring, based on calendar 

date, to the bark of susceptible trees before the adult females oviposit. Larval A. anxius chew 

through the chorion directly into the wood beneath, so insecticides must be applied to the 

trunks before oviposition to exploit this small window of larval vulnerability. However, insecticide applications based on calendar date do not consider unusually cool or warm spring 

weather that may alter adult emergence. The timing of spray applications against A. anxius

can be improved through the use of a degree-day (DD) model with a base temperature of 

10oC that predicts adult emergence. Research conducted in Ohio, USA, indicated that 10% 

of adult A. anxius emerge when the mean degree-day accumulation reaches 235.7DD. Insecticides should be applied at that time for this pest.

Sampling Procedure

Begin monitoring degree-day accumulation on 1 April using a base temperature of 10oC. 

Expect 10% of adult A. anxius to emerge when the mean degree-day accumulation reaches 

235.7DD. Insecticide treatments should be applied to the bark of white-barked birches when 

temperatures reach this threshold. 
268

Note

This model was developed for use in Ohio, U.S.A., and may not be accurate for other regions. Use with caution until validated in other areas.
269

Emerald Ash Borer

Agrilus planipenis Fairmaire

Coleoptera: Buprestidae

Crook, D. J.; Khrimian, A.; Francese, J. A.; Fraser, I.; Poland, T. M.; Sawyer, A. J.; Mastro, 

V. C. 2008. Development of a host-based semiochemical lure for trapping emerald ash 

borer Agrilus planipennis (Coleoptera: Buprestidae). Environmental Entomology 37: 356-

365. 

Objective

To improve detection of A. planipennis using pheromone-baited prism traps.

Abstract

Emerald ash borer is an established, exotic woodborer attacking healthy green (Fraxinus 

pennsylvanica Marsh.), black (F. nigra Marsh.), and most importantly, white ash (F. americana L.) in northern North America. Tree mortality is high and rapid. Attempts at eradication appear to be failing, but nonetheless a useful survey system is required for this pest. 

Research has shown that A. planipennis are attracted to the color purple and a purple box trap 

baited with ash logs was developed by Francese et al. (2005) (see our review in this volume) 

to detect adult populations. Further research indicates that purple, three-sided prism traps 

baited with volatiles found in ash bark distillates catch significantly more A. planipennis per 

m2 of trapping surface than the original four-sided box trap baited with ash logs. No differences in trap catch were found among five sizes of the prism trap. Placing the purple prism 

traps in the mid-canopy of ash trees appeared to enhance their efficacy in attracting A. planipennis adults. Prism traps baited with Phoebe oil, a steam distillate from Brazilian walnut 

(Phoebe porosa Mez.), caught significantly more beetles than unbaited traps or traps baited 

with Manuka oil, a distillate from New Zealand tea tree (Leptospermum scoparium J.R. and 

G. Forst). Phoebe oil contains 7-epi-sesquithujene and an unidentified compound not found 

in Manuka oil, along with four sesquiterpenes shared with Manuka oil and also identified 

from green ash bark. These commercial oils are readily available and offer a viable alternative to the difficult and expensive option of synthesizing single or multiple component sesquiterpene lures for A. planipennis adults. 

Sampling Procedure

Purple panels are the most efficient color in trapping adult A. planipennis (Francese et al. 

2005). Tie three 35 x 60 cm panels of corrugated purple plastic (0.26 cm thick; Coroplast, 

Dallas, TX) together to form an open-ended prism with a 0.63 m2 surface area, or fold a 
270

single sheet of plastic to these dimensions. Coat the outside of each panel with insect trapping glue (The Tanglefoot Company, Grand Rapids, MI). 

Use lures formulated in polypropylene “bubble cap” or “pouch” devices (Synergy 

Semiochemicals, Burnaby, BC, Canada) to deliver a release rate of 50 mg Phoebe oil/day. 

Individual release rates of the identified components in Phoebe oil are approximately as follows:

Identified component Release rate per day

α-cubene 0.2 mg

α-copaene 3.0 mg

Trans-β-carophyllene 0.5 mg

α-humulene 0.8 mg

7-epi-sesquithujene 0.75 mg

Hang each Phoebe oil lure from a black carabiner (5.6 cm long) from a hole punched in 

the bottom of one of the trap panels. Place traps 13 m above ground, preferably within the 

canopy of ash trees where adults are active (Francese et al. 2007). Traps should be separated 

by 40 m. Check traps weekly for adults. Replace lures after 4 weeks. 

References

Francese, J. A.; Fraser, I.; Lance, D. R.; Mastro, V. C. 2007. Developing survey techniques 

for emerald ash borer: the role of trap height and design. In: Mastro, V. C.; Lance, D.; 

Reardon, R.; Parra, G. editors. Proceedings of the Emerald ash borer research and Asian 

longhorned beetle research and development review meeting. October 29– November 2 

2006; Cincinnati, OH. FHTET-2007-04. Morgantown, WV: U.S. Department of Agriculture, Forest Service; 72–73. 

# Francese, J. A.; Mastro, V. C.; Oliver, J. B.; Lance, D. R.; Youssef, N.; Lavallee, S. G. 

2005. Evaluation of colors for trapping Agrilus planipennis (Coleoptera: Buprestidae). 

Journal of Entomological Science 40: 93-95.
271

Emerald Ash Borer

Agrilus planipennis Fairmaire

Coleoptera: Buprestidae

Francese, J. A.; Mastro, V. C.; Oliver, J. B.; Lance, D. R.; Youssef, N.; Lavallee, S. G. 2005. 

Evaluation of colors for trapping Agrilus planipennis (Coleoptera: Buprestidae). Journal of 

Entomological Science 40: 93-95.

Objective

To evaluate trap color as an attractant for flying A. planipennis in detection surveys.

Abstract

Emerald ash borer is an established, exotic woodborer attacking healthy green (Fraxinus 

pennsylvanica Marsh.), black (F. nigra Marsh.), and most importantly, white ash (F. americana L.) in northern North America. Tree mortality is high and rapid. Attempts at eradication appear to be failing, but nonetheless a useful survey system is required for this pest. 

There is evidence that other Agrilus spp. use color, sound, and semiochemicals to locate 

potential hosts. This study reports the effect of trap color and placement height on catch rates 

of adult A. planipennis. 

Purple sticky traps caught significantly more borers than all other colors tested at both the 

1.8 m and 6.1 m placement heights of the traps. Moreover, significantly greater numbers of 

borers were caught at the 1.8 m height than at the 6.1 m height with purple-colored traps. 

Therefore, purple-colored traps suspended at the 1.8 m height should be sufficient at detecting borers until useful semiochemicals are developed for this pest. This type of detection 

survey is useful because flying adult borers are recovered before large numbers of borer exit 

holes and crown dieback are detected in host trees.

Sampling Procedure

Purple panels are the most efficient color in trapping adult A. planipennis. Using plastic 

cable ties, tie four 37.5 x 60 cm corrugated plastic panels (0.26 cm thick; Coroplast, Dallas, 

TX) together to form an open-ended box. Attach a stainless steel umbrella rig (Zing Products, Westport, MA) to each open end of the box to maintain the shape of the trap. Attach 

two ash logs, 3.8 cm in diameter and 50 cm long, to the steel spreaders inside the box as an 

attractant. Bait logs should be enclosed entirely by the box trap. Suspend the trap 1.8 m 

above ground from the crown of an ash tree. Coat the outside of each panel with Pestick 

insect trapping glue (Hummert International, Earth City, MO). Check traps bi-weekly for 

adults.
272

Notes

No information was provided regarding the spacing of box traps within a given area. 

An improved trap consisting of a three-sided prism of corrugated purple plastic and baited 

with attracted volatiles is now recommended for detection of A. planipennis (Crook et al. 

2008; see our review in this volume), but the original box trap baited with ash logs may be 

useful for some surveys. Also see our review of Francese et al. (2008) in this volume for additional information regarding trap placement for A. planipennis.

References

# Crook, D. J.; Khrimian, A.; Francese, J. A.; Fraser, I.; Poland, T. M.; Sawyer, A. J.; Mastro, 

V. C. 2008. Development of a host-based semiochemical lure for trapping emerald ash 

borer Agrilus planipennis (Coleoptera: Buprestidae). Environmental Entomology 37: 356-

365. 

# Francese, J. A.; Oliver, J. B.; Fraser, I.; Lance, D. R.; Youssef, N.; Sawyer, A. J.; Mastro, V. 

C. 2008. Influence of trap placement and design on capture of the emerald ash borer (Coleoptera: Buprestidae). Journal of Economic Entomology 101: 1831-1837.
273

Emerald Ash Borer

Agrilus planipenis Fairmaire

Coleoptera: Buprestidae

Francese, J. A.; Oliver, J. B.; Fraser, I.; Lance, D. R.; Youssef, N.; Sawyer, A. J.; Mastro, V. 

C. 2008. Influence of trap placement and design on capture of the emerald ash borer (Coleoptera: Buprestidae). Journal of Economic Entomology 101: 1831-1837.

Objective

To evaluate horizontal and vertical trap placement for flying A. planipennis in detection surveys; to evaluate trap color within and outside of an ash woodlot; to evaluate the efficacy of 

trap design.

Abstract

Emerald ash borer is an established, exotic woodborer attacking healthy green (Fraxinus 

pennsylvanica Marsh.), black (F. nigra Marsh.), and most importantly, white ash (F. americana L.) in northern North America. Tree mortality is high and rapid. Attempts at eradication appear to be failing, but nonetheless a useful survey system is required for this pest. 

There is evidence that other Agrilus spp. use color, sound, and semiochemicals to locate 

potential hosts. This study reports the effect of trap design, color, and placement height and 

distance in relation to an ash woodlot on catch rates of adult A. planipennis.

Purple traps caught significantly more A. planipennis than red or white traps. Purple traps 

caught significantly more A. planipennis in fields and along edges of woodlots than inside 

woodlots. Four-sided box traps caught more A. planipennis than crossvane traps, while twopanel traps and three-sided prism traps caught as many beetles as the other trap designs. The 

box, panel, and prism traps caught more beetles per square meter of flat trap surface than 

the crossvane traps. In general, the authors recommend the use of the prism trap for sturdiness, ease of construction, and economy. Prism traps hung at 13 m caught more beetles than 

those hung at 6 m, and traps hung at 13 and 6 m caught more beetles than those set at 1.5 m. 

Greater captures in traps hung at canopy height (13 m) likely reflect the adult activity of A. 

planipennis occurring predominately in the canopy. Placing traps at canopy height requires 

additional effort (e.g., the use of a bucket truck or tree-climbing equipment), but increases the 

chances of detecting the presence of A. planipennis within a stand.

 

Sampling Procedure

Purple is the most efficient color in trapping adult A. planipennis. Three-sided prism traps 

are recommended for sturdiness, ease of construction, and economy. Construct traps out of 
274

corrugated plastic panels (0.26 cm thick; Coroplast, Dallas, TX) (Fig. 2; also described in 

our review of Crook et al. 2008 in this volume). Attach a three-arm modified stainless steel 

umbrella rig spreader (Zing Products, Westport, MA) to the top of each trap using plastic 

cable ties (Fig. 2). Coat the outside of each panel with Pestick insect trapping glue (Hummert International, Earth City, MO). Set each prism trap at a height of 13 m in the midcrown 

of an ash tree. Traps can be checked easily if hung from a rope attached to the umbrella 

spreader and using a pulley attached to an upper branch. Space traps by 15 m. Deploy traps 

before adult flight activity during the summer (or before June) and remove trapped adults 

each week. 

Purple panel traps may be useful if traps are set at heights of 3 m or lower. Attach two 15.2 

cm wide by 91.4 cm tall panels of corrugated purple plastic (0.26 cm thick; Coroplast, Dallas, 

TX) to a steel rebar pole (1.27 cm diam.) (Fig. 1). The top of the first panel should be hung 

at 0.91 m above ground and the top of the second panel should be hung above the first on the 

same pole at 3.0 m above ground, with 2.1 m in between the two panels. Coat each side of 

both panels with insect trapping glue. Set purple panel traps outside of woodlots containing ash trees, either along the edges of the woodlot or in adjacent fields. Deploy traps before 

adult activity during the summer (or before June) and remove trapped adults each week. 

If using the four-panel box trap (described in our review of Francese et al. 2005 in this volume), hang traps at 1.5 m above ground using L-shaped steel rebar poles (1.27 cm diam.) in 

fields adjacent to woodlots. Coat the outside of each panel with Pestick insect trapping glue. 

Space traps by 15 m. Deploy traps before adult flight activity during the summer (or before 

June) and remove trapped adults each week. 

Notes

Although not specified, consider deploying prism traps in the canopies of ash trees directly 

along the edges of a woodlot and not within the woodlot. 

The results of a greater trap capture of A. planipennis at 13 and 6 m heights are in contrast 

with those of Francese et al. 2005, who reported that greater numbers of adults were caught 

at the 1.8 m height than at the 6.1 m height using purple-colored box traps (see our review in 

this volume).

References

# Crook, D. J.; Khrimian, A.; Francese, J. A.; Fraser, I.; Poland, T. M.; Sawyer, A. J.; Mastro, 

V. C. 2008. Development of a host-based semiochemical lure for trapping emerald ash 

borer Agrilus planipennis (Coleoptera: Buprestidae). Environmental Entomology 37: 356-

365. 

# Francese, J. A.; Mastro, V. C.; Oliver, J. B.; Lance, D. R.; Youssef, N.; Lavallee, S. G. 

2005. Evaluation of colors for trapping Agrilus planipennis (Coleoptera: Buprestidae). 

Journal of Entomological Science 40: 93-95.
275

Fig. 1. Purple panel trap used for emerald ash borer. Figure modified from Fig. 1, Francese 

et al. 2008, Journal of Economic Entomology 101: 1831–1837.
276

Fig. 2. Emerald ash borer prism trap used in 2006 and 2007. (A) Schematic of a prism trap 

cut from a single sheet of corrugated plastic. White circles represent holes to be used for 

attachment points to an iron rebar pole if the trap is to be hung at ground level. Black circles 

represent holes to be used for attachment points to a wire trap hanger if the trap is to be hung 

from a tree or other high place. (B) Diagram of the assembled trap showing both a high setup 

(left) and a low setup (right). All units are in centimeters.

Fig. 2 reprinted as fair us under 17 USC § 107.
277

Red Oak Borer

Enaphalodes rufulus Haldeman

Coleoptera: Cerambycidae

Crook, D. J.; Fierke, M. K.; Mauromoustakos, A.; Kinney, D. L.; Stephen, F. M. 2007. Optimization of sampling methods for within-tree populations of red oak borer, Enaphalodes 

rufulus (Haldeman) (Coleoptera: Cerambycidae). Environmental Entomology 36: 589-594.

Objective

To develop a systematic sampling procedure that accurately estimates within-tree population 

densities of E. rufulus in northern red oak using a minimal number of samples. 

Abstract

Red oak borer, Enaphalodes rufulus Haldeman, is a serious native pest of red oak, Quercus 

rubra L., and has been recently associated with oak decline, particularly in the Ozark National Forest in Arkansas. This cerambycid can kill stressed trees, such as those affected by 

oak decline. An accurate method of estimating E. rufulus populations through destructive 

sampling has been developed (Fierke et al. 2005a), but it is labor intensive. A nondestructive, rapid estimation survey procedure has also been developed to classify tree populations 

of E. rufulus during epidemics (Fierke et al. 2005b). 

Another sampling plan has been developed based on the minimum number of samples determined necessary for statistical accuracy while substantially reducing the labor requirement 

of Fierke et al. (2005a). This plan differs from Fierke et al. (2005b) in that it is also useful 

for non-epidemic population levels. Systematic sampling of seven bolts cut from boles of 

trees infested with E. rufulus produced root mean square errors below the 25% threshold 

of the measured parameters of current generation galleries and live E. rufulus. This plan is 

recommended as an optimal sampling method to monitor E. rufulus densities with acceptable 

statistical accuracy, but requiring fewer samples and reduced labor. 

Sampling Procedure

Fell selected northern red oaks. Assess the presence of E. rufulus attack and emergence holes 

along the trunk to determine the height of infestation on each tree. Cut each trunk into seven 

0.5-m sample bolts starting at 1.5–2.0 m high and continuing at 20, 40, 50, 60, 80, and 90% 

of the infested tree bole. Dissect bolts to determine the number of current generation galleries in the phloem and the density of live E. rufulus present in the tree. Bolts should be 

refrigerated at 2°C until dissection. 
278

Note

The authors suggest that a more appropriate sampling method might be determined from their 

data tables for studies needing a higher level of statistical precision or when other population 

parameters, such as emergence holes in the bark or previous generation galleries in the heartwood, are being assessed. See the original publication for details.

References

# Fierke, M. K.; Kinney, D. L.; Salisbury, V. B.; Crook, D. J.; Stephen, F. M. 2005a. A rapid 

estimation procedure for within-tree populations of red oak borer (Coleoptera: Cerambycidae). Forest Ecology and Management 215: 163-168.

Fierke, M. K.; Kinney, D. L.; Salisbury, V. B.; Crook, D. J.; Stephen, F. M. 2005b. Development and comparison of intensive and extensive sampling methods and preliminary 

within-tree population estimates of red oak borer (Coleoptera: Cerambycidae) in the Ozark 

Mountains of Arkansas. Environmental Entomology 34: 184-192.
279

Red Oak Borer

Enaphalodes rufulus Haldeman

Coleoptera: Cerambycidae

Donley, D. E.; Rast, E. 1984. Vertical distribution of the red oak borer, Enaphalodes rufulus

(Coleoptera: Cerambycidae), in red oak. Environmental Entomology 13: 41-44.

Objective

To develop a sampling method for E. rufulus based on its vertical distribution during attacks 

in immature stands of red oak (Quercus spp.).

Abstract

Red oak borer, Enaphalodes rufulus Haldeman, attacks both red and white oak tree groups 

(Quercus spp.) in the eastern USA and southeastern Canada. Larvae feed on the phloem and 

sapwood of host trees and cause large economic losses in oak-hickory forests. Initial attacks 

are difficult to detect as only pinhole wounds are found on the bark, but emerging adults 

leave noticeable exit holes measuring 10–14 mm in diameter. Evaluation of damage and 

execution of control measures for E. rufulus have been based on sampling the 5 m basal section of trees between 10 and 30 cm dbh as this pest attacks trees at a uniform height and dbh. 

There is a need to develop a sampling method that will determine the E. rufulus population 

from the vertical distribution in immature oak trees, since attack density is inversely related 

to size for trees of this age.

Infested red oaks (Quercus rubra L., Q. coccinea Muench., and Q. velutina Lam.) in immature stands in Indiana and Pennsylvania were examined for infestation by moderate populations of E. rufulus. Attack density, attacks per tree, and attacks per unit of bark surface were 

measured on cut trees among nine dbh classes (10.0, 12.5, 15.0, 17.5, 20.0, 22.5, 25.0, 27.5, 

and 30.0 cm). Attack height, attacks per tree, or attacks per unit of bark surface area did not 

vary between the two locations. Trees with 10–30 cm dbh had suitable wood for infestation 

from the ground line (0.06 m) to 22.4 m high, but E. rufulus attacked stems only between 

0.07–17 m. Only 6 attacks were observed on limbs. Attack density decreased with tree size 

but attack height increased with dbh class. The authors concluded that at least 75% of E. 

rufulus attacks occur in the economically important basal 5 m of red oaks. 

Sampling Procedure

Identify infested red oaks by inspecting the bark from the ground up to ≈23 m. Number each 

tree before cutting it down and sectioning the trunk into transportable pieces. In the lab, debark all sections with diameters of ≥7.5 cm outside the bark, which is the wood habitable by 
280

E. rufulus. Record the height of each attack from the original groundline. Record the total 

attacks per tree and the vertical distance (height) of each infestation in the stem to the original groundline for each dbh class. Calculate the cumulative frequency of attack heights from 

all dbh classes by plotting attack height against the cumulated percentage of attacks.

Notes

Red oak stands were selected for this study based on the presence of moderate E. rufulus

populations that warranted control measures, and the results of this study may differ from 

stands with low or high populations. No significant differences were detected in the number 

of E. rufulus attacks between peeled and unpeeled trunk sections, so managers may consider 

leaving the bark on the trunk sections to save time. Trees between 10–30 cm dbh are considered brood trees (Donley 1981) and are removed for control of E. rufulus.

References

Donley, D. E. 1981. Control of the red oak borer by removal of infested trees. Journal of 

Forestry 79: 731-733.
281

Red Oak Borer

Enaphalodes rufulus Haldeman

Coleoptera: Cerambycidae

Fierke, M. K.; Kinney, D. L.; Salisbury, V. B.; Crook, D. J.; Stephen, F. M. 2005a. A rapid 

estimation procedure for within-tree populations of red oak borer (Coleoptera: Cerambycidae). Forest Ecology and Management 215: 163-168.

Objective

To develop a nondestructive procedure that quickly estimates the population levels of red oak 

borer in northern red oak, Quercus rubra L., during epidemics.

Abstract

Red oak borer, Enaphalodes rufulus Haldeman, is a serious native pest of red oak, Quercus 

rubra L., and has been recently associated with oak decline, particularly in the Ozark National Forest in Arkansas. This cerambycid can kill stressed trees, such as those affected by oak 

decline. An accurate method of estimating E. rufulus populations through destructive sampling has been developed (Fierke et al. 2005b), but it is labor intensive. A rapid estimation 

survey procedure was therefore developed to classify tree populations of E. rufulus during 

epidemics. 

Overall, the nondestructive rapid estimation survey procedure correctly classifies red oak 

borer populations in ≥85% of sampled trees. Tree crown condition and red oak borer emergence holes on the basal 2 m of tree bole are used to classify trees into one of three categories ranging from Class 1 (low infestation) to Class III (high infestation). This plan requires 

<2 minutes to conduct on each tree, whereas the procedure of Fierke et al. (2005b) required 

>100 hours per tree.

Sampling Procedure

Assess the percentage of crown dieback on an individual tree using the procedures described 

by Starkey et al. (2000) and in USDA (2001). Assign a crown condition class (CCC) to 

the tree following the rating scale for percent defoliation in the crown as presented in Table 

1. Count the number of red oak borer emergence holes in the first 2 m of height on the tree 

bole. Assign a basal emergence hole (BEC) class to the tree based on the number of tallied 

emergence holes following the scale presented in Table 1. Sum the CCC and BEC to obtain 

the rapid estimation index (REI), which ranges in value from 0¬–7. Reference Table 1 to 

determine the rapid estimation procedure (REP) class for the individual tree based on the 

calculated REI value. Class I and Class II trees are considered to have a low and moderate 
282

infestation level, respectively. Class III trees have a high infestation of E. rufulus with dead 

or mostly dead crowns, numerous emergence holes, and imminent tree mortality. Land managers can use these REP classes to estimate E. rufulus population levels and the extent of the 

infestation throughout stands of red oak, and base management decisions on this information.

Notes

This procedure is useful for rapidly estimating populations of red oak borer in a tree during 

epidemics. However, the authors caution against using it with endemic populations as Class 

I trees in this study had greater numbers of E. rufulus than expected given the levels previously reported in the literature for endemic conditions. Further work is required to determine 

the number of trees needed to sample in a stand or forest in order to estimate red oak borer 

populations at specified levels of fixed precision. See Crook et al. (2007) in this volume for 

information regarding a sampling plan for E. rufulus using a minimal number of samples.

References

# Crook, D. J.; Fierke, M. K.; Mauromoustakos, A.; Kinney, D. L.; Stephen, F. M. 2007. Optimization of sampling methods for within-tree populations of red oak borer, Enaphalodes 

rufulus (Haldeman) (Coleoptera: Cerambycidae). Environmental Entomology 36: 589-594.

# Fierke, M. K.; Kinney, D. L.; Salisbury, V. B.; Crook, D. J.; Stephen, F. M. 2005b. Development and comparison of intensive and extensive sampling methods and preliminary 

within-tree population estimates of red oak borer (Coleoptera: Cerambycidae) in the Ozark 

Mountains of Arkansas. Environmental Entomology 34: 184-192.

Starkey, D.; Mangini, S.; Oliveria, F.; Clarke, S.; Bruce, B.; Kertz, R.; Menard, R. 2000. 

In: Forest Health Evaluation of Oak Mortality and Decline on the Ozark National Forest, 1999. Alexandria, LA: U.S. Department of Agriculture, Forest Service, Forest Health 

Protection.

(USDA) U.S. Department of Agriculture, Forest Service. 2001. Forest inventory and analysis 

Southern Research Station Field Guide. Asheville, NC: U.S. Department of Agriculture, 

Forest Service, Southern Research Station.
283

Table 1. Rapid estimation procedure variables assessed in the field include crown condition 

and number of basal emergence holes. 

Crown 

condition

CCC Emergence 

holes <2m

BEC REI REP

class

<1% dieback 0 0 0 0, 1 Class I

1–33% dieback 1 1–5 1

34–66% dieback 2 6–20 2 2, 3, 4 Class II

67–99% dieback 3 >20 3

Dead 4 >4 Class III

Summation of these variables yields indices that were used to classify trees into REP infestation history classes. 

Abbreviations: CCC, crown condition class; BEC, basal emergence hole class; REI, rapid estimation index 

(CCC + BEC).

Table 1 reprinted with permission from Elsevier Limited, granted June 8, 2009. 
284

Mountain Pine Beetle

Dendroctonus ponderosae Hopkins

Coleoptera: Curculionidae

Safranyik, L.; Linton, D. A. 2002. Line transect sampling to estimate the density of lodgepole 

pine currently attacked by mountain pine beetle. Inform. Rpt. BC-X-392. Victoria, B. C.: 

Natural Resources Canada, Canadian Forest Service, Pacific Forestry Centre; 10 p. 

Objective

To develop an efficient line transect method for estimating densities of trees infested by D. 

ponderosae, as judged by signs of recent beetle activity.

Abstract

Mountain pine beetle, Dendroctonus ponderosae Hopkins, is a serious pest of lodgepole pine, 

Pinus contorta Dougl. ex Loud, in western North America. Other hosts include ponderosa 

pine, Pinus ponderosa Dougl. ex Laws., sugar pine, Pinus lambertiana Dougl., and western 

white pine, Pinus monitcola Dougl. ex D. Don. Generally D. ponderosae will attack host 

pines stressed by mechanical or fire damage, growing on poor sites, attacked by pathogens or 

other insect pests, or are overmature or overcrowded. During outbreaks of D. ponderosae, 

even healthy trees may be attacked and extensive tree mortality may occur.

Regression analysis was used to determine the probability of correctly identifying lodgepole 

pines attacked by D. ponderosae located at varying distances from a transect line. Two cruisers independently walked and counted all infested trees along 36 50-m transects. Fixed plots 

were established over the center of each transect and all trees within each plot were examined 

for infestation. Prism sampling was also conducted at three locations along the transect line.

The relationship between the probability of detecting an infested tree and the perpendicular 

distance of an infested tree from the transect line was used to construct a detection curve (i.e. 

, numbers of infested trees observed along the transect vs. the distance of observed trees from 

the transect). The detection curve was used to estimate û using the following equation:

Â = n/2Lû

where Â = the general estimator of infested trees per unit area, n = the number of trees observed with an active infestation, L = transect length in meters, and û = an estimate of one 

half of the effective strip width about the line transect. Line transects conducted in southern 

British Columbia indicated that the density of currently infested lodgepole pines per hectare 

could be determined as Â = 10,000nb/La2

, where b and a are the slope and intercept of the 
285

detection curve and the other variables are defined as above. The estimate of Â did not differ 

from estimates from the fixed plots and prism plots, but the line transect method took only 

2–3 minutes on a 50-m line compared to approximately 15 minutes for conducting fixed or 

prism plots along the same ground. While experience in recognizing recently infested trees 

did not appear to affect the average density of infested trees identified in this study, each 

cruiser should develop a personal detection curve and equation for estimating infested trees 

using this method. If cruisers work in pairs, the detection curve data for each pair can be 

combined into a single curve.

Sampling Procedure

Each cruiser should develop a personal detection function by conducting preliminary transect 

surveys. The total length of the preliminary transect should be long enough that a cruiser 

sights a minimum of 200 infested trees, but this minimum can be divided among separate 

transects. 

Set a transect by pulling a 50-m tape taut on the ground. Walk along the length of this 

transect and tally all trees with signs of recent D. ponderosae activity on one side of the 

transect. Keep to a walking pace without stopping to examine any trees in detail. When a 

suspected infested tree is seen along the transect, record the distance of the tree along the 

50-m tape from the start of the transect line. At the end of the transect, return to the starting 

point and proceed along the other side in the same manner. 

Count and record the number of recently infested trees and their distances from the starting 

point on both sides of the transect, then measure and record the perpendicular distance of 

each observed tree from the transect line. Examine each observed tree to confirm that it is 

currently infested or not. Determine the maximum distance that a confirmed, attacked tree 

was observed from along the transect. Add 2 m to this distance to calculate the half-width of 

a fixed plot to be centered over the transect length. Examine each tree within the fixed plot 

and record whether it has a current infestation or not. Make note of all trees that cruisers did 

not observe from the transect.

Calculate the number of currently infested and noninfested trees per hectare using the fixed 

plot data. Sort the transect data into total trees per 2-m wide strips up to a maximum distance 

of 20 m from the transect line. Calculate the average proportions of confirmed infested trees, 

infested trees not observed from the transect, and trees misidentified as infested. 

Construct a histogram for the mean number of confirmed, currently infested lodgepole pine 

trees detected by a cruiser along the transect (Y) against the distance of the trees from the 

transect line in 2-m increments (X). Estimate the value of û by integrating the detection 

curve from zero to a perpendicular distance equal to the x-intercept of a linear regression.

To use this method in practice, randomly establish transect lines through the area of interest 

and record the number of observed infested trees and the distance walked along the transect 
286

for each observation. The appropriate total length of a transect line (L) can be estimated using the following formula:

L = (c/CV2

)(L1/n1)

where c = the number of observed infested trees, CV2

 = the desired coefficient of variation, 

L1 = the length of a preliminary walk along L, and n1 = the number of observed infested trees 

along L1

. 

Estimate the value of û as the integral from 0 to the point where the regression line intercepts 

the x-axis (X = a/b), with a = the intercept and b = the slope from the linear regression of the 

developed detection curve, so that û = a2

/2b. Estimate the density of infested trees (Â) per 

hectare using the equation Â = 10,000n/2Lû, where n = the number of currently infested trees 

observed along the transect, including any trees that might prove to be uninfested with later 

examination, and L = the length of the transect.

Note

If trees are tallied on only one side of the transect, use the formula Â = [n/2Lû]2. Refer to the 

original publication for more detail on creating the detection curve and its use in estimating 

û. A logistic regression may provide a better fit than a simpler linear regression.
287

Mountain Pine Beetle

Dendroctonus ponderosae Hopkins

Coleoptera: Curculionidae

Wulder, M. A.; Dymond, C. C.; Erickson, B. 2004. Detection and monitoring of the mountain pine beetle. Info. Rpt. BC-X-398. Victoria, B.C.: Natural Resources Canada, Pacific 

Forestry Centre; 24 p. 

Objective

To describe a method of estimating the population trend and overwintering mortality for D. 

ponderosae.

Abstract

Mountain pine beetle, Dendroctonus ponderosae Hopkins, is a serious pest of lodgepole pine, 

Pinus contorta Dougl. ex Loud, in western North America. Other hosts include ponderosa 

pine (Pinus ponderosa Dougl. ex Laws.), sugar pine (Pinus lambertiana Dougl.), and western 

white pine (Pinus monitcola Dougl. ex D. Don). Generally D. ponderosae attacks host pines 

stressed by mechanical or fire damage, growing on poor sites, attacked by pathogens or other 

insect pests, or are overmature or overcrowded. Even healthy trees may be attacked and 

extensive tree mortality may occur during outbreaks of D. ponderosae.

Fixed-size bark samples offer a statistically valid means of determining the population trend 

and overwintering mortality of D. ponderosae within a stand (Shore 1985). Typically bark 

samples are 900 cm2

 and sample plots are <1 ha in size. The number of eggs and larvae, 

pupae and adults, and galleries present in the sample are used to calculate the average trend 

ratio within a stand of a given number of sampled trees. Stands can be classified as having 

an increasing, decreasing, or static population. Trend ratios from the fall and the spring can 

be used to determine the overwintering mortality of D. ponderosae occurring within a stand. 

Trend ratios should be used only to support the findings of aerial or ground surveys, but not 

replace them.

Sampling Procedure

Of particular interest are newly infested green trees near red attacked trees. These green 

attacked trees currently hold brood and will serve as the source of new infestations. Green 

attack trees should be located through ground surveys by starting near red trees and using 

a prism or strip cruising technique. These surveys should be done between September and 

October, and later again the subsequent spring. As potential brood trees are found in stands, 

remove a fixed size area of bark (ca. 900 cm2

) from each tree. Tally the number of eggs and 
288

larvae, pupae and adults, and galleries present in the bark sample. Calculate the average 

trend ratio (r) for each stand using the following formula

 

r = ( ) ( ) y + o / g

t

∑

t

where y = the number of eggs and larvae, o = the number of pupae and adults, g = the number 

of galleries present in each sample, and t = the number of sampled trees. If r >4.0, then the 

population is increasing. If r <2.6, the population is decreasing. If 4.0 < r >2.6, then the 

population is static. Use the estimated trend ratio to support data collected in aerial and 

ground surveys, but not as a definitive infestation estimate for a given population. 

If of interest, determine the percent of overwintering mortality (r) in each stand using the 

following equation:

 

r = (r _ fall − r _ spring)x100

r _ fall

where r = the average trend ratio, r_fall = r determined in the fall, and r_spring = r 

determined in the spring. 

Note

The average trend ratio and percent of overwintering mortality should be used to support the 

interpretation of aerial and ground surveys of D. ponderosae infestations. They should not 

be used as the primary technique of assessing infestations. Ground crews doing brood survey 

work should be trained well and their work should be checked periodically for consistency 

and accuracy.

Reference

Shore, T. 1985. Forest insect and disease survey, Pacific Region: general instruction manual. 

Victoria, B.C.: Government of Canada, Canadian Forestry Service, Pacific Forest Research 

Center; 125 p.
289

Douglas-fir Beetle

Dendroctonus pseudotsugae Haldeman

Coleoptera: Curculionidae

Negrón, J. F.; Schaupp, W. C.; Johnson, E. 2000. Development and validation of a fixedprecision sequential sampling plan for estimating brood adult density of Dendroctonus 

pseudotsugae (Coleoptera: Scolytidae). Canadian Entomologist 132: 119-133.

Objective

To develop a sequential estimation plan for the assessment of D. pseudotsugae population 

trends. 

Abstract

The Douglas-fir beetle, Dendroctonus pseudotsugae Hopkins, is a serious insect pest of 

Douglas-fir, Pseudotsuga menziesii (Mirb.) Franco, in western North America. Large populations of this bark beetle can destroy vast areas of Douglas-fir. Sampling procedures for 

Douglas-fir beetle attacking Douglas-fir are time-consuming. A sequential estimation procedure was developed to address this issue.

Log variance was related positively to log mean D. pseudotsugae brood adults (log variance 

= 0.491 + 1.687(log mean); r2

 = 0.54). The intercept and slope from this equation was fitted 

into the equation given by Green (1970) to produce the sequential sampling stop lines at the 

0.1, 0.2, and 0.3 levels of fixed precision. The number of samples required to estimate D. 

pseudotsugae populations, across all beetle densities, was 91, 20, and 8 at the 0.1, 0.2, and 

0.3 levels of fixed precision, respectively. 

Sampling Procedure

Identify the stand or area to be sampled. Throughout the stand, randomly select trees successfully attacked by D. pseudotsugae and with a diameter of at least 25.4 cm. Remove a 

vertically oriented sample of bark 30.5 long by 15.25 cm wide from the south side of the tree. 

Collect samples at 1.37 m above ground. Chill samples until they can be processed. Dissect and examine each bark sample and tally all brood adults and gallery starts present in the 

sample. Reference Fig. 2 and continue sampling additional trees until the line of the desired 

level of fixed precision is crossed, at which point sampling is stopped. A precision level of 

0.2 may be adequate for monitoring D. pseudotsugae populations in order to make management decisions. 
290

After sampling has stopped, calculate the emergence ratio for the stand using the following 

formula:

 Stand emergence ratio = cumulative number of brood adults 

(cumulative number of gallery starts x 2 beetles)

Land managers should consider management strategies for D. pseudotsugae when increasing 

emergence ratios signal the presence of growing pest populations.

Note

Data were collected from a few sites in Colorado. Use this plan with caution until it can be 

validated with data from other areas where D. pseudotsugae is present.

Fig. 2. Sequential sampling stop lines for Dendroctonus pseudotsugae brood adults in 30.5 cm x 15.2 cm (0.046 

m2) samples with precision levels (C) of 0.2 and 0.3, Pike-San Isabel National Forest, Colorado, 1997–1998.

Figure 2 modified and reproduced with permission by the authors, granted April 16, 

2009. The C = 0.1 level of fixed precision, found in the original publication, has been 

omitted due to the large sample sizes required to estimate densities of brood adults.
291

Spruce Beetle

Dendroctonus rufipennis (Kirby)

Coleoptera: Curculionidae

Fiddick, R. L. 1978. Use of felled trap trees as a supplementary technique for reducing spruce 

beetle infestation. Information Rpt. BC-P-23. Victoria, B. C.: Environment Canada, Forestry Service, Pacific Forest Research Centre; 2 p. 

Objective

To describe a method of predicting increased attacks by D. rufipennis.

Abstract

Spruce beetle, Dendroctonus rufipennis (Kirby), is a serious pest of spruce trees (Picea spp.) 

in North America. Englemann spruce (Picea engelmannii Parry ex Engelm.), white spruce 

[Picea glauca (Moench) Voss] and Sitka spruce (Picea sitchensis Carr.) are commonly attacked. Large-diameter, mature trees are preferred hosts. The life cycle of D. rufipennis

typically takes two years to complete, but can be accelerated if summers are long, hot, and 

dry. Outbreaks of D. rufipennis may last 2–5 years and can produce high levels of mortality 

in mature spruce trees. Felled trap trees can be used to predict an increase in attacks by D. 

rufipennis, especially during hot, dry years that accelerate brood development. If pupae and 

adults represent >50% of the brood found in 20–25 trap trees, there is an increased chance of

D. rufipennis attacking more trees the subsequent spring.

Sampling Procedure

Locate and identify infested spruce stands with ground surveys. In the fall, examine brood 

development on 20–25 spruce trees by removing 0.05 m2 of bark on each tree and tallying 

the percentage of D. rufipennis in each life stage present (i.e., larva, pupa, or adult). If pupae 

and adults represent >50% of the brood present on 20–25 trees, there is an increased chance 

of more trees being attacked the subsequent spring. Hot, dry summers tend to accelerate 

beetle development and favor building populations the following year.
292

Spruce Beetle

Dendroctonus rufipennis (Kirby)

Coleoptera: Curculionidae

Hansen, E. M.; Bentz, B. J.; Munson, A. S.; Vandygriff, J. C.; Turner, D. L. 2006. Evaluation 

of funnel traps for estimating tree mortality and associated population phase of spruce 

beetle in Utah. Canadian Journal of Forest Research 36: 2574-2584.

Objective

To relate trap capture of D. rufipennis to tree mortality within a given area and predict the 

size of a local population.

Abstract

Spruce beetle, Dendroctonus rufipennis (Kirby), is a serious pest of spruce trees (Picea spp.) 

in North America. Englemann spruce (Picea engelmannii Parry ex Engelm.), white spruce 

[Picea glauca (Moench) Voss] and Sitka spruce (Picea sitchensis Carr.) are commonly attacked. Large-diameter, mature trees are preferred hosts. The life cycle of D. rufipennis

typically takes two years to complete, but can be accelerated if summers are long, hot, and 

dry. Outbreaks of D. rufipennis may last 2–5 years and can produce high levels of mortality 

in mature spruce trees.

Researchers in Utah related captures of D. rufipennis in Lindgren funnel traps (Lindgren 

1983) to population phase and a relative measure of tree mortality for the current and subsequent year in Englemann spruce. Populations of D. rufipennis could be separated into 

endemic (<2 mass-attacked trees per ha) or epidemic (≥2 mass-attacked trees per ha) phases 

based on total trap capture. Capture of ≈842 beetles from a single trap over a flight period 

is considered a threshold delineating the endemic and epidemic phases of D. rufipennis for 

the current and subsequent year relative to trap use. The epidemic phase is further indicated 

by the presence of spillover attacks on susceptible host trees within 10 m of the funnel traps. 

Total trap capture was not useful in predicting the number of trees that were attacked, nor 

could suboutbreak populations D. rufipennis be distinguished using this technique. Land 

managers can use the population phase of D. rufipennis and associated level of tree mortality 

predicted by total trap capture as an aid in making management decisions.

Sampling Procedure

Bait each 16 unit Lindgren funnel trap with a two-component lure consisting of racemic 

frontalin and +5/–95 α-pinene (PheroTech, Inc., Delta, B.C.). Load frontalin and α-pinene 
293

separately in 400 μL and a 1.8 mL centrifuge tubes, respectively. Release rates for frontalin 

and α-pinene should be 2.8 and 1.3 mg per day at 20°C, respectively. Include an insecticidal 

strip in each trap to kill any beetles attracted to the trap.

Multiple funnel traps should be installed in stands of Engelmann spruce, with >800 m between traps to avoid inter-trap bias. Hang traps so the trap cup is 1.5 m above ground and 

>0.25 m from any bole. Service traps weekly during the operational period. Traps should 

remain in place throughout the entire flight period of D. rufipennis, approximately late May 

through mid-August. Replace α-pinene lures in mid-summer.

Total trap capture of under ≈842 beetles per trap indicates that the population is in an endemic phase, whereas a trap capture of ≈842 beetles or more indicates that the population is in an 

epidemic phase. The relative density of mass-attacked trees and the predicted level of tree 

mortality associated with endemic and epidemic populations of D. rufipennis are provided 

below:

Total trap capture 

over flight period

Population

phase

Associated density of

mass-attacked trees

Predicted host tree mortality

>842 beetles

per trap

Endemic <2 mass-attacked trees 

per ha

Few, if any, trees within 10 ha infested in current or subsequent year

<842 beetles

per trap

Epidemic ≥2 mass-attacked

trees per ha

Substantial tree mortality within a 10 

ha area in the subsequent year, if not 

the current year

At the end of the flight period, examine susceptible host trees within 10 m of the funnel trap 

for evidence of mass-attack by D. rufipennis. Spillover attacks within 10 m of the funnel 

trap are a further indication of an epidemic population phase.

Note

Not all populations of D. rufipennis may respond in similar ways to the combination of lures 

and trap type used in this study. The recommendation made from this study should be used 

with caution until verified in other regions.

Reference

# Lindgren, B. S. 1983. A multiple funnel trap for scolytid beetles (Coleoptera). Canadian 

Entomologist 115: 299-302.
294

Scolytid Beetles

Coleoptera: Curculionidae

Lindgren, B. S. 1983. A multiple funnel trap for scolytid beetles (Coleoptera). Canadian Entomologist 115: 299-302.

Objective

To evaluate the effectiveness of a multiple funnel trap in capturing scolytid beetles. 

Abstract

The multiple funnel trap, also known as the Lindgren funnel trap, revolutionized the trapping of ambrosia and bark beetles. The trap is constructed of suspended, vertically aligned 

funnels baited with pheromone lure and directing captured insects into a collection jar at the 

bottom of the trap. This design minimizes the loss of attracted beetles, a problem encountered with window flight traps when beetles resume flight after colliding with the vertical 

panes. Beetles were captured more easily by the funnel trap than the perforated entrances of 

stovepipe traps. Furthermore, the multiple funnel trap collected beetles in a jar at the bottom 

of the trap, avoiding the need for the adhesive used in sticky traps. Traps are reusable and are 

serviced quickly, saving both time and money. 

Sampling Procedure

The multiple funnel trap is now available commercially, but was originally constructed of 

suspended, vertically aligned funnels (16 cm tall; 20 cm upper diameter; 3.5 cm lower diameter) made from 0.2 mm vinyl sheets. Funnels were stapled to three twill tapes (16 mm 

wide) at 7.5 cm intervals. Traps consisting of eight funnels provided 0.33 m2

 trapping surface, which could be adjusted as needed by adding or removing funnels, or by using funnels 

of a different size. 

Each trap could be collapsed for portability. A pheromone lure was placed under an inverted 

plastic tray (30 cm diameter) set above the top funnel. The lid of a collection container was 

attached at the neck of the lowest funnel and the jar screwed onto it. The collection container 

could hold a killing substance and preservative, or filled with suitable material to hold live 

insects. A hole piercing the side of the collection container allowed excess rainwater to drain. 

Traps are hung from sturdy hooks welded to iron pipe or rods. Avoid installing traps under 

trees where leaves may fall and clog the funnels. 
295
296
297

glossary
298
299

Terms Definition

accuracy The degree of closeness of a measured or calculated quantity 

to its actual (true) value.

aesthetic injury level An action threshold for treatment based on aesthetic impacts 

on a plant

aestivate To enter into diapause during a warm or dry season

apical dominance In plants, the tendency of an apical bud to produce hormones 

that suppress the growth of the lateral buds below it on a 

stem.

beat sheet A hand-held tool constructed of a square piece of light colored fabric and often supported by a cross frame which is 

used to catch insects when sampling.

binomial sampling A sampling method that involves recording only the presence or absence of members of the population being sampled 

(such as an insect pest) on a sample unit (such as a leaf), 

rather than counting the numbers of individuals; presence/

absence sampling.

chorion The outer shell or covering of an arthropod egg.

coefficient of variation A statistical measure of the dispersion of data points in a data 

series around the mean. It represents the ratio of the standard 

deviation to the mean, and it is a useful statistic for comparing the degree of variation from one data series to another, 

even if the means are drastically different from each other.

components of variance When factors being tested are random, a type II ANOVA 

model is used to determine how much of the variance in an 

experiment is due to difference in treatment means, and how 

much is due to random error about these means.

Glossary
300

conspecific An organism belonging to the same species of another

crown Branches, leaves and reproductive structures extending from 

the trunk or main stems; highest point of tree.

cultivar A cultivated variety or strain of a plant produced by horticultural techniques and not normally found in wild populations.

degree days A measurement of thermal units required for growth and 

development of an arthropod, other cold-blooded animals, or 

plants. Daily Mean temperature - base development temperature provides degree-days for that day. Add degree-days over 

many days to get to an accumulated amount that can represent a certain stage of growth specific to that organism.

delta trap A triangular shaped trap with a sticky catch surface often 

used to attract lepidopteran pest species with a pheromone 

lure.

density-dependent 

mortality

Rate of mortality that is influenced by increasing population 

density (either positively or negatively). Ex. The greater the 

population of a host, the higher the percentage of hosts parasitized by a parasitoid.

density-independent

mortality

Rate of mortality that is not influenced by its density (either 

positively or negatively). Ex. Extreme weather factors can 

kill a percentage of a population regardless of the population 

size

diapause A period of genetically controlled arrested development and 

reduced metabolic rate, during which growth, differentiation 

and metamorphosis cease.

eclose For an insect to emerge from the cuticle of the pupa, the 

cocoon, or puparium; or in hemimetabolous insects, from the 

cuticle of the last nymphal instar.

economic injury level The lowest population density of a pest that will cause economic damage; amount of pest injury, which will justify the 

cost of control.
301

economic threshold The level of pest population or damage level one needs to 

treat at to prevent the reaching of the economic injury level.

endemic Native or limited to a specific economic injury level.

enumerative sampling plan A sampling plan that incorporated continuous values as sampling measures.

frass Solid larval excrement exuded from the anus.

fundatrices In aphids, the wingless parthnogenic female that emerges in 

the spring for overwintering eggs.

gall Abnormal growth of plant tissues stimulated by animal or 

other biotic organism activity on or in the plant tissue.

hyperbolic curve Any of a set of six functions related, for a real or complex 

vairable x, to the hyperbola in a manner analogous to the 

relationship of the trigonometric functions to a circle.

immunoassay A technique that uses the binding of antibodies to antigens to 

identify and measure the presence of certain substances.

indicator plants A species whose presence, absence, or relative well-being in 

a given environment is indicative of the health of its ecosystem as a whole. The phenological state of plant development 

can be used to time or synchronize with other biotic activites.

leaf plastochron index Way of measuring the age of plants and leaves on a morphological rather than a temporal time scale.

linear regression Models the relationship between two variables by fitting a 

linear equation to observed data. One variable is considered 

to be an explanatory variable, and the other is considered 

to be a dependent variable. The most common method for 

fitting a regression line is the method of least-squares. This 

method calculates the best-fitting line for the observed data 

by minimizing the sum of the squares of the vertical deviations from each data point to the line.
302

logarithmic regression Regression done on a log scale. Data may be transformed to 

a log scale when the variance of Y changes with changing 

values of X. 

logistic regression A regression modle for binomially distributed response/dependent variables that is useful for modeling the probability 

of an event occurring as a function of other factors.

operculum A lid or cover.

oviposition The act of laying eggs.

ovisacs An egg-containing capsule.

parthenogenesis An asexual form of reproduction found in females where 

growth and development of embryos occurs without fertilization by a male.

pheromone trap Any of various containers baited with sexual scent, used to 

ure and entrap pest or disrupt mating habits.

precision The degree to which further measurements or calcualtions 

show the same or similar results.

prism sampling A point sampling method commonly used to inventory trees 

in which each tree has a given plot size in relation to its size. 

It is easily applied with an optical wedge prism and is appropriate for determining basal area and density, as well as for 

assessing volume and stand structure.

probit A unit of measurement of statistical probability based on 

deviations from the mean of normal distribution.

progredientes One of two generations of hemlock woolly adelgid found in 

late spring developing on hemlock trees. Progredients are 

comprised of both wingless (parthenogenic) and winged offspring (sexuparae), which develop simultaneously.

radiograph A photographic image produced on a radiosensitive surface 

by radiation other than visible light.
303

raster A data structure for maps based on grid cells.

scouting The monitoring of pest populations and crop development 

to help growers make sound pest management decisions 

through knowledge of the pest and natural enemy populations. If used carefully, scouting will help prevent crop damage while eliminating unnecessary pest control treatments.

semiochemical Any substance, produced by an organism that provokes a 

response in individuals of the same or different species.

sequential estimation A method of estimating a parameter by analyzing a sample 

just large enough to ensure a previously chosen degree of 

precision. The fundamental technique is to take a sequence of 

samples, the outcome of each sampling determining the need 

for another sampling. The procedure is terminated when the 

desired degree of precision is achieved.

sequential sampling A sampling method in which the number of samples is not 

fixed in advance.

sessile Attached or fastened, incapable of moving from place to 

place; attached directly without a stem or petiole.

sexuparae A winged portion of the progredientes generation of hemlock 

woolly adelgid found on hemlock trees in late spring. Sexupara, when fully developed, will leave hemlocks in search 

of a spruce species on which to oviposit. No suitable species 

is present in North America so this portion of the population 

dies before sexual reproduction occurs.

sistentes One of two generations of hemlock woolly adelgid found in 

autumn and winter following a summer aestivation. This is a 

wingless, parthenogenetic generation that gives rise to progredientes.

stand An arbitrarily determined cohort of trees considered as a 

single management unit.

stratified sampling Grouping members of the population into relatively homogeneous subgroups before sampling.
304

strip cruising Where the diameters at breast height (dbh) of all trees within 

a certain distance of a center line are tallied and the volume 

of wood on the area sampled and then on the total area of the 

forest stand are then calculated from these diameter measurements.

Taylor’s power law An equation used to related variance to the mean population 

size: S2

 = aμb, where a and b are constants, a is a sampling 

parameter while b is an index of aggregation characteristic of 

the species, s2 is the variance, μ is the mean.

terminal leader The primary shoot of a plant.

transect A path along which one records and counts occurrences of 

the phenomenon of study (e.g. plants noting each instance).

type I error Rejection of the null hypothesis when it is in fact true.

type II error Not rejecting the null hypothesis when it is false.

vector Mathematicial: Mapping data, which is stored as points, lines 

and/or polygons.

whorl An arrangement of three or more leaves, petals or branches 

radiating from a single node.

wing trap A type of paper/cardboard based pheomone trap used for 

sampling insect pests.
305

References
306
307

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PERMISSION ACKNOWLEDGMENTS
326
327

Permission Acknowledgments

Sources for each figure and table are indicated as they occurred in the text at the request of 

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In addition, the following agencies and journals granted us permission to reprint previously 

published materials:

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9C3
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* Permission kindly granted gratis.
329

INDEX
330
331

Index

A

Acari: Tetranychidae 

 Spruce spider mite, Oligonychus ununguis (Jacobi) 211, 213, 215

 degree-day monitoring, control decision-making 211

 adult survey, beat sheet method, damage prediciton 213

 adult survey, sequential sampling plan with known precision 215

Acleris gloverana (Walsingham).

 See Lepidoptera: Tortricidae, Western blackheaded budworm

Actebia fennica (Tauscher)

 See Lepidoptera: Noctuidae, Black army cutworm

Adelges piceae (Ratzeburg)

See Hemiptera: Adelgidae, Balsam woolly adelgid.

Adelges tsugae Annand

 See Hemiptera: Adelgidae, Hemlock woolly adelgid

Adult survey 3, 5, 23, 29, 32, 52, 54, 56, 60, 64, 66, 74, 76, 78, 80, 82, 84, 86, 89, 94, 111, 114, 162, 164, 213, 

215, 217, 221, 226, 229, 239, 244, 253, 257, 269, 271, 273, 289, 292, 294

 aesthetic injury level 239

 binomial sampling plan 226

 binomial samping plan with known precision 221

 board and pitfall trap 5 

 control decision making 32, 54, 89, 94, 114, 226, 239, 289 

 damage prediction 111, 213, 217

 differentiation between congeneric males 74

 exclusion of Operophtera bruceata 76

 fixed sampling plan with known precision 244

 hazard rating 3

 hypothetical damage prediction 29

 inhibition of Operophtera bruceata 78

 Lindgren funnel traps 294

 lure-baited trap 269

 monitoring population trends 52, 162, 292

 monitoring when to start larval surveys 164

 monitoring system 64

 optimal sample sizes 239 

 oviposition trap 60, 80, 82

 pheromone-baited traps 3, 23, 29, 32, 52, 54, 64, 74, 76, 78, 86, 89, 94, 111, 114, 162, 164

 purple box trap 271

 purple prism trap 273

 sequential sampling plan 289

 sequential sampling with known precision 215, 229

adult sampling with known precision 289

aerial damage survey 166

aesthetic injury level 119, 121, 239

Agrilus anxius Gory

 See Coleoptera: Buprestidae, Bronze birch borer
332

Agrilus planipenis Fairmaire

 See Coleoptera: Buprestidae, Emerald ash borer

Akers, R. C.; Nielsen, D. G. 267

Alfaro, R. I. 11

Alsophila pometaria (Harris)

 See Lepidoptera: Geometridae, Fall cankerworm

alternate host survey 234

Anderbrant, O.; Löfqvist, J.; Jönsson, J.; Marling, E. 53

Anisota senatoria (J.E. Smith)

 See Lepidoptera: Saturniidae, Orangestriped oakworm

Antonelli, A. L.; Campbell, R. L. 26

Aphrophora saratogensis (Fitch)

 See Hemiptera: Cercopidae, Saratoga spittlebug

artificial pupation shelter 91

Asaro, C.; Berisford, C. W. 30, 32

Asaro, C.; Cameron, R. S.; Nowak, J. T.; Grosman, D. M.; Seckinger, J. O.; Berisford, C. W. 29

Asaro, C.; Dalusky, M. J.; Berisford, C. W. 30

B

Bagworm

 See Lepidoptera: Psychidae

Balsam fir sawfly

 See Hymenoptera: Diprionidae

Balsam twig aphid

 See Hemiptera: Apididae

Barbara colfaxiana (Kearfott)

 See Lepidoptera: Tortricidae, Western conifer seed bug

Bates, S. L.; Borden, J. H. 255

Bates, S. L.; Lait, C. G.; Borden, J. H.; Kermode, A. R. 253, 256

Batzer, H. O.; Hastings, A. R. 139

beat-sheet method 184, 211

Betulaphis brevipilosa Börner

 See Hemiptera: Drepanosiphidae, Birch aphid

binomial sampling plan 221

 with known precision 226

Birch aphid

 See Hemiptera: Drepanosiphidae

Black army cutworm

 See Lepidoptera: Noctuidae

Black larch aphid

 See Hemiptera: Aphididae

Black vine weevil

 See Coleoptera: Curculionidae

Blatt S. E.; Borden, J. H. 250

Brandt, J. P. 16

Bronze birch borer

 See Coleoptera: Buprestidae

brood survey 291

 monitoring population trends 291

Brown, C. E. 81, 83

Bruce spanworm

 See Lepidoptera: Geometridae
333

Bryant, D. G. 217, 218

bud, shoot and root insects 1–33

 See also Coleoptera: Curculionidae

 Diptera: Cecidomyiidae

 Lepidoptera: Gelechiidae

 Lepidoptera: Tortricidae

Buffam, P. E.; Carolin, Jr., V. M. 170

burlap band method 68

C

Callipterinella calliptera (Hartig)

 See Hemiptera: Drepanosiphidae, Birch aphid

Carolin, V. M.; W. K. Coulter. 180

Choristoneura biennis Freeman

 See Lepidoptera: Tortricidae, Two-year-cycle spruce budworm

Choristoneura fumiferana (Clemens)

 See Lepidoptera: Tortricidae, Spruce budworm

Choristoneura occidentalis (Freeman)

 See Lepidoptera: Lymantriidae, Western spruce budworm

Choristoneura occidentalis Freeman

 See Lepidoptera: Tortricidae, Western spruce budworm

Choristoneura pinus pinus Freeman

 See Lepidoptera: Tortricidae, Jack pine budworm

Chrysomela scripta Fabricus

 See Coleoptera: Chrysomelidae - Cottonwood leaf beetle

Ciesla, W. M. 192

Cinaria laricifex (Fitch)

See Hemiptera: Aphididae, Black larch aphid

Cochran, W. G. 21

Coffelt, M. A.; Schultz, P. B. 121, 123

Coleoptera: Buprestidae

Bronze birch borer, Agrilus anxius Gory 267

 degree-day monitoring, control decision-making 267

Emerald ash borer, Agrilus planipennis Fairmaire 269, 271, 273

 adult survey, lure baited-trap 269

 adult survey, purple box trap 271

 adult survey, purple prism trap 273

Coleoptera: Cerambycidae

Red oak borer, Enaphalodes rufulus Haldeman 277, 279, 281

 dissected bolt method, population estimation 277

 non-destructive method, population estimation 281

 within stand population estimation 279

Coleoptera: Chrysomelidae

Cottonwood leaf beetle, Chrysomela scripta Fabricus 37, 39

 damage prediction, control decision-making, egg mass survey 37

Elm Leaf Beetle, Pyrrhalta (=Xanthogaleruca) luteola (Muller) 37, 39, 41, 42, 45, 48, 49

 degree-day monitoring, optimum sample sizes, control decision-making 42,

 damage prediction, control decision-making, egg mass survey with known precision 45

 degree-day monitoring for peak density of young instars, control decision-making 48

 egg mass survey with known precision, degree-day monitoring, control decision-making 49

Coleoptera: Curculionidae

Black vine weevil, Otiorhynchus sulcatus (F.) 5
334

 adult survey, board and pitfall traps 5

Cranberry white grub, Phyllophaga anxia (LeConte) 7

 larval survey, control decision-making 7

Douglas-fir beetle, Dendroctonus pseudotsugae Haldeman 289

 adult survey with known precision, pheromone-baited trap, monitoring population trends 291

Lodgepole terminal weevil, Pissodes terminalis Hopkins 14

 preliminary damage survey on spruce, optimum sample sizes 14

Mountain pine beetle, Dendroctonus ponderosae Hopkins 284, 287

 damage survey, line trasect method 284

 population estimation, fixed bark sample 287

Scolytid beetles 294

 adult survey, Lindgren funnel traps 294

Seedling debarking weevil, Hylobius congener Dalla Torre, Shenkling, & Marshall 3

 adult survey, pheromone-baited traps, hazard rating 3

Spruce beetle, Dendroctonus rufipennis (Kirby) 291, 292

 brood survey, monitoring population trends 291

 adult survey, pheromone-baited traps, monitoring population trends 292

White grubs, Phyllophaga spp. 9

 larval survey, regression equations, damage predicion 9

White pine weevil, Pissodes strobi (Peck) 11, 14

 damage survey on spruce, sequential sampling plan 11

 preliminary damage survey on spruce, optimum sample sizes 14

Coleoptera: Scarabaeidae

Cole, W. E. 117

comparison of three different survey methods 198

comphrehensive review 

 of egg mass sampling methods 135

 of general sampling mehods 107

 of sampling methods of all life stages 148

Contarinia oregonensis Foote

 See Diptera: Cecidomyiidae, Western conifer seed bug

control decision making 7, 27, 32, 37, 39, 42, 45, 48, 49, 51, 54, 89, 94, 116, 119, 121, 203, 211, 217, 226, 231, 

233, 237, 239, 249, 267, 289

Corythucha ciliata (Say)

 See Hemiptera: Tingidae, Sycamore lace bug

Costa, S.; Onken, B. 221

Cottonwood leaf beetle

 See Coleoptera: Chrysomelidae

Cranberry White grub

 See Coleoptera: Scarabaeidae

Croesia semipurpurana (Kearfott) Freeman

 See Lepidoptera: Tortricidae, Oak leaftier

Crook, D. J.; Fierke, M. K.; Mauromoustakos, A.; Kinney, D. L.; Stephen, F. M. 277, 282

Crook, D. J.; Khrimian, A.; Francese, J. A.; Fraser, I.; Poland, T. M.; Sawyer, A. J.; Mastro, V. C. 269, 272, 274

Cuming, F. G. 81, 83

cut branch samples 66

cut cone surface mthod 262

Cydia strobilella (L.)

 See Diptera: Anthomyiidae, Spruce seed moth

D
335

Dahlsten, D. L.; Norick, N. X.; Wenz, J. M.; Williams, C. B.; Rowney, D. L. 92

Dahlsten, D. L.; Rowney, D. L.; Copper, W. A.; Wenz, J. M. 91

Dahlsten, D. L.; Rowney, D. L.; Lawson, A. B. 47

Dahlsten, D. L.; Rowney, D. L.; Tait, S. M. 42, 48, 50

damage prediction(s) 37, 45, 56, 84, 111, 117, 123,166, 180, 188, 192, 211, 234, 257

damaged cone and seed survey 260, 262

damaged seed survey 253, 255

damage survey 56, 132, 142, 284

 line transect method 284

damage survey on spruce 11

 Daterman, G. E.; Livingston, R. L.; Wenz, J. M.; Sower, L. L. 95

Daterman, G. E.; Wenz, J. M.; Sheehan, K. A. 94

defoliating insects 35–199

 See also Coleoptera: Chrysomelidae

 Hymenoptera: Diprionidae

 Hymenoptera: Tenthredinidae

 Lepidoptera: Geometridae

 Lepidoptera: Lasiocampidae

 Lepidoptera: Lymantriidae

 Lepidoptera: Noctuidae

 Lepidoptera: Notodontidae

 Lepidoptera: Pieridae

 Lepidoptera: Psychidae

 Lepidoptera: Saturniidae

 Lepidoptera: Tortricidae

 Thysanoptera: Thripidae

defoliation survey with known precision 190

degree-day monitoring 27, 42, 49, 211, 231, 237, 267

degree-day monitoring for peak density of young instars 48

degree-day monitoring to predict cumulative adult trap catch 25

Dendroctonus ponderosae Hopkins

See Coleoptera: Curculionidae, Mountain pine beetle

Dendroctonus pseudotsugae Haldeman 

See Coleoptera: Curculionidae, Douglas-fir beetle

Dendroctonus rufipennis (Kirby)

 See Coleoptera: Curculionidae, Spruce beetle

density per kg fresh weight method 159

Dioryctria abietivorella (Grote)

 See Lepidoptera: Pyralidae, Western conifer seed bug

Diptera: Anthomyiidae

Spiral spruce cone borer, Strobilomyia neanthracina (Michelsen) 249

 egg survey, control decision-making 249

Spruce seed moth, Cydia strobilella (L.) 249

Diptera: Cecidomyiidae

Spruce gall midge, Mayetiola piceae (Felt) 16

 gall survey, sequential sampling plan, damage prediction 16 

Western conifer seed bug, Contarinia oregonensis Foote 257

 

Dobbs, R. C.; Edwards, D. G. W.; Konishi, J.; Wallinger, D. 261

Dobesberger, E. J. 63

Dombrosky, S. A.; Schowalter, T. D. 257

Donley, D. E. 280
336

Donley, D. E.; Rast, E. 279

Douglas-fir beetle

See Coleoptera: Curculionidae

Douglas fir tussock moth

See Lepidoptera: Lymantriidae

Dreistadt, S. H.; Dahlsten, D. L. 45

Dreistadt, S. H.; Dahlsten, D. L.; Rowney, D. L.; Tait, S. M.; Yokota, G. Y.; Copper, W. A. 48, 50

E

Eastern hemlock looper

See Lepidoptera: Geometridae

economic injury level 39

egg and egg mass sampling; egg and egg mass survey 37, 39, 42, 45, 49, 51, 62, 70, 72, 87, 89, 91, 109, 117, 

123, 125, 129, 153, 166, 170, 177, 188, 192, 233, 249

 aerial damage survey 166

 branch tip estimate 177

 control decision-making 37, 39, 45, 51, 89, 233, 249, 

 damage predictions 37, 117, 123, 129, 166, 188, 192

 degree-day monitoring 42 

 economic injury level 39

 fixed-radius plot 87

 hot water extraction technique 129

 monitoring population trends 170

 optimum sample sizes 42

 oviposition trap 62

 pheromone-baited trap 89

 processing lichen samples 70

 processing moss and bark samples 70

 sequential sampling plan 153

 timed visual method 109

 timed-walk method

egg mass and cocoon survey 91

 artificial pupation shelter 91

 monitoring population trends 91

egg mass survey with known precision 49

 control decision-making 49

 degree-day monitoring 49

Eggen, D.; Abrahamson, L. P. 88

Eidt, D. C.; Embree, D. G.; Smith, C. C. 74, 77, 79

Elm leaf beetle

See Coleoptera: Chrysomelidae

Emerald ash borer

 See Coleoptera: Buprestidae

Enaphalodes rufulus Haldeman

 See Coleoptera: Cerambycidae: Red oak borer

Euceraphis betulae (Koch)

 See Hemiptera: Drepanosiphidae, Birch aphid

European pine sawfly

 See Hymenoptera: Diprionidae

Evenden, M. L. 64, 65

Evenden, M. L.; Borden, J. H.; Van Sickle, G. A. 64, 65

Evenden, M. L.; Borden, J. H.; Van Sickle, G. A.; Gries, G. 64
337

F

Fall cankerworm

 See Lepidoptera: Geometridae

Fang, Y.; Hart, E. R. 37, 41

Fang, Y.; Pedigo, L. P.; Colletti, J. P.; Hart, E. R. 38, 39

Felt, E. P. 17

Fettes, J. J. 133, 191

Fettig, C. J.; Dalusky, M. J.; Berisford, C. W. 33

Fiddick, R. L. 291

Fidgen, J. G.; Legg, D. E.; Salom, S. M. 226

Fierke, M. K.; Kinney, D. L.; Salisbury, V. B.; Crook, D. J.; Stephen, F. M. 278, 281, 282

Fitzpatrick, S. M.; Troubridge, J. T.; Peterson, B. 74, 77

fixed-radius plot 87

fixed sampling plan with known precision 244

Fleischer, S. J.; Carter, J.; Reardon, R.; Ravlin, F. W. 88

Fleischer, S. J.; Ravlin, F. W.; Reardon, R. C. 88

Fletcher, V. E. 14

Fogal, W. H.; Larocque, G. 260

Fogal, W. H.; Plowman, V. C. 261

Fondren, K. M.; McCullough, D. G. 231

forecasting larval populations 104

Forest Tent Caterpillar

See Lepidoptera: Lasiocampidae

Fowler, G. W.; Simmons, G. A. 135

Fowler, R. F.; Wilson, L. F. 9

Francese, J. A.; Fraser, I.; Lance, D. R.; Mastro, V. C. 270

Francese, J. A.; Mastro, V. C.; Oliver, J. B.; Lance, D. R.; Youssef, N.; Lavallee, S. G. 270, 271, 274

Francese, J. A.; Oliver, J. B.; Fraser, I.; Lance, D. R.; Youssef, N.; Sawyer, A. J.; Mastro, V. C. 272, 273

Freeman, T. N. 171

G

Gall survey 16

 damage prediction 16

 sequential sampling plan 16

Gray, T. G.; Shepherd, R. F.; Wood, C. S. 125, 130

Gray, T. G.; Slessor, K. N.; Shepherd, R. F.; Grant, G. G.; Manville, J. F. 23

Gypsy moth

See Lepidoptera: Lymantriidae

H

Hajek, A. E.; Dahlsten, D. L. 239

Hall, R. J.; Volney, W. J. A.; Wang, Y. 186

Hansen, E. M.; Bentz, B. J.; Munson, A. S.; Vandygriff, J. C.; Turner, D. L. 292

Hanson, P. D. 251

Harris, J. W. E.; Collis, D. G.; Magar, K. M. 127, 173

Harris, J. W. E.; Dawson, A. F.; Brown, R. G. 172

harvest decision-making 262

Hébert, C.; St. Antoine, L. 60, 61, 80, 81, 82, 83

Hébert, C.; St. Jobin, L.; Auger, M.; Dupont, A. 62

Hemiptera: Adelgidae

Balsam woolly adelgid, Adelges piceae (Ratzenburg) 217
338

 adult survey, optimum sample sizes, control decision-making 217

Hemlock woolly adelgid, Adelges tsugae Annand 221, 226

 adult survey, binomial sampling plan with known precision 221

 adult survey, binomial sampling plan, control decision-making 226

Hemiptera: Aphididae

Balsam twig aphid, Mindarus abietnus Koch 231

 degree-day monitoring, control decision-making 231

Black larch aphid, Cinaria laricifex (Fitch) 229

 nymphal and adult survey, sequential sampling plan with known precision 229

Wooly pine aphid, Schizolachnus piniradiatae (Davidson) 233

 eg mass survey, control decision-making 233

Hemiptera: Cercopidae

Saratoga spittlebug, Aphrophora saratogensis (Fitch) 234

 alternate host survey, damage predictions 234

Hemiptera: Coreidae

Western conifer seed bug, Leptoglossus occidentalis Heidemann 250, 253, 255

 nymphal and adult survey, damaged seed survey, loss estimate 253

 damaged seed survey, immunoassay method 255

 nymphal and adult survey, damage predictions 257

Hemiptera: Diaspidae

Obscure scale, Melanaspis obscura (Comstock) 237

 degree-day monitoring, control decision-making 237

Hemiptera: Drepanosiphidae

Birch aphid, Betulaphis brevipilosa Borner 239

 degree-day monitoring, control decision-making 239

Birch aphid, Callipterinella calliptera (Hartig) 239

 degree-day monitoring, control decision-making 239

Birch aphid, Euceraphis betulae (Koch) 239

 degree-day monitoring, control decision-making 239

Hemiptera: Tingidae

Sycamore lace bug, Corythucha ciliata (Say) 244

 nymphal and adult survey, fixed sampling plan with known precidion 244

Hemlock woolly adelgid

See Hemiptera: Adelgidae

Herms, D. A. 203

Heterocampa guttivata (Walker)

Lepidoptera: Notodontidae, Saddled prominent 114

Heterocampa manteo (Doubleday)

See Lepidoptera: Notodontidae

Hill, A. S.; Berisford, C. W.; Brady, U. E.; Roelofs, W. L 30

Horn, K. F.; Farrier, M. H.; Wright, C. G.; Nelson, L. A. 244

hot water extraction technique 125, 129

Hylobius congener Dalla Torre, Shenkling, & Marshall

 See Coleoptera: Curculionidae

Hymenoptera: Diprionidae

Balsam sawfly, Neodiprion abietis (Harris) 51, 52

 egg mass survey, control decision-making 51

 adult survey, pheromone-baited traps, monitoring population trends 52

Hymenoptera: Tenthredinidae

Larch sawfly, Pristiphora erichsonii (Hartig) 54, 56
339

 damage survey, sequential sampling plan, damage prediction 56

 Yellow-headed Spruce Sawfly, Pikonema alaskensis (Rohwer) 54

 adult survey, pheromone-baited trap, control decision-making 54

Hymenoptera: Torymidae

 Western conifer seed bug, Megastimus spemotrophus (Wachtl) 257 

hypothetical damage prediction 29

I

immunoassay method 255

indicator plant phenology 203

Introduced basswood thrips

See Thysanoptera: Thripidae

Ives, W. G. H.; Prentice, R. M. 56

Ives, W. G. H.; Warren, G. L 8

J

Jack pine budworm

 See Lepidoptera: Tortricidae

Johnston, H. R.; Eaton, C. B. 10

K

Kleintjes, P. K.; Lemoine, E. E.; Schroeder, J.; Solensky, M. J. 232

Kolodny-Hirsch, D. M 88

Kozak, A. 262

Kozak, A.; Sziklai, O.; Griffith, B. G.; Smith, J. H. G. 263

Kulman, H. M. 233

Kuno, E. 21

L

Lait, C. G.; Bates, S. L.; Kermode, A. R.; Morrissette, K. K.; Borden, J. H. 254

Lambdina fiscellaria (Guenée)

See Lepidoptera: Geometridae, Eastern hemlock looper

Lambdina fiscellaria lugubrosa (Hulst)

 See Lepidoptera: Geometridae, Western hemlock looper

Larch sawfly. See Hymenoptera: Tenthredinidae

larval survey 7, 9, 20, 66, 84, 96, 98, 116, 119, 123, 127, 146, 159, 166, 172, 174, 179, 184, 194

 3-tree sampling 127

 aesthetic injury level 119

 beat-sheet method 184 

 control decision making 7, 116, 119

 damage prediction 9, 84

 density per kg fresh weight method 159

 mid-crown branch tip estimates 174

 mid-crown estimate 184

 mid-crown estimates from lower crown samples 98

 monitoring population trends from non-outbreak phase 96 

 optimum sample 98, 179

 regression equations 9

 sampling criteria 172

 sequential sampling plan 84, 194

 statistical error corrections 84 
340

 whole branch from midcrown level 146

larval and adult survey 66

 cut branch samples 66 

 pheromone-baited trap 66

 roadside sampling 66 

larval and pupal survey with known precision 20 

 optimum sample sizes 20

Lawson, A. B.; Dahlsten, D. L. 49

Lepesoma lecontei (Casey)

 See Coleoptera: Cirulionidae, Western conifer seed bug

Lepidoptera: Gelechiidae

Jeffrey pine needle miner, Coleotechnites sp. 20

 larval and pupal survey with known precision, optimum sample sizes 20

Lepidoptera: Geometridae

Bruce spanworm, Operophtera bruceata (Hulst) 74, 76, 78, 80

 adult survey, pheromone-baited trap, differentiation between congeneric males 74

 adult survey, pheromone-baited trap, exclusion of O. bruceata 76

 adult survey, pheromone-baited trap, inhibition of O. bruceata 78

 adult survey, oviposition trap 80

Eastern hemlock looper, Lambdina fiscellaria (Guenee) 62

 egg survey, oviposition trap 62

Fall cankerworm, Alsophila pometaria (Harris) 60

 adult survey, ovipostion trap 60

Western hemlock looper, Lambdina fiscellaria lugubrosa (Hulst) 64, 66, 68, 70, 72

 adult survey, pheromone-baited trap, monitoring system 64

 larval and adult survey, pheromone-baited trap, cut-branch samples, roadside sampling 66

 pupal survey, burlap band method 68

 egg survey, processing moss and bark samples 70

 egg survey, processing lichen samples 72

Winter moth, Operophtera brumata (L.) 74, 76, 78, 82, 84

 adult survey, pheromone-baited trap, differentiation between congeneric males 74

 adult survey, pheromone-baited trap, exclusion of O. bruceata 76

 adult survey, pheromone-baited trap, inhibition of O. bruceata 78

 adult survey, oviposition trap 80

 larval survey, sequential sampling plan, damage prediction 84

Lepidoptera: Lasiocampidae

Forest tent caterpillar, Malacosoma disstria (Hubner) 86

 adult survey, pheromone-baited trap

Lepidoptera: Lymantriidae

Douglas fir tussock moth, Orgyia pseudotsugata (McDonnough) 91, 94, 96, 98, 102, 104, 107, 109

 egg mass and cocoon survey, artificial pupation shelter, monitoring population trends 91

 adult survey, pheromone-baited trap, control decision-making 94

 larval survey, monitoring population trends from nonoutbreak phase 96

 larval survey, midcrown estimates from lower crown samples, optimim sample sizes 98

 review of typical phases of outbreak 102

 cocoon survey, forecasting larval populations, monitoring population trends 104

 comprehensive review of general sampling methods, pest management time line 107

 egg mass survey, timed visual method 109

Gypsy moth, Lymantria dispar (L.) 87, 89

 egg mass survey, timed-walk method, fixed-radius plot 87

 egg mass and adult survey, pheromone-baited trap, control decision-making 89

Western spruce budworm, Choristoneura occidentalis (Freeman) 98

Lepidoptera: Noctuidae
341

Black army cutworm, Actebia fennica (Tauscher) 111

 adult survey, pheromone-baited trap, damage prediction 111

Lepidoptera: Notodontidae

Saddled prominent, Heterocampa guttivata (Walker) 114

 adult survey, pheromone-baited trap, control decision-making 114

Variable oaklead caterpillar, Heterocampa manteo (Doubleday) 116

 larval survey, control decision-making 116

Lepidoptera: Pieridae

Pine butterfly, Neophasia menapia (Felder & Felder) 117

 egg survey, damage predictions 117

Lepidoptera: Psychidae

Bagworm, Thyridopteryx ephemeraeformis (Haworth) 119

 larval survey, aesthetic injury level, control decision-making 119 

Lepidoptera: Pyralidae

Western conifer seed bug, Dioryctria abietivorella (Grote) 257

Lepidoptera: Saturniidae

Orangestriped oakworm, Anisota senatoria (J.E. Smith) 121, 123

 frass survey, aesthetic injury level, control decision-making 121

 egg mass and young larval survey, damage prediction 123 

Lepidoptera: Tortricidae

European pine shoot moth, Rhyacionia buoliana (Denis & Schiffermuller) 23, 25

 adult survey, pheromone-baited trap 23

 degree-day monitoring to predict cumulative adult trap catch 25

Jack pine budworm, Choristoneura pinus pinus Freeman 186, 188, 190

 risk-rating susceptible stands 186

 egg mass survey, damage predictions 188

 defoliation survey with known precision, optimal sample sizes 190

Nantucket pine tip moth, Rhyacionia frustrana (Comstock) 27, 29, 32

 pheromone-baited traps, degree-day monitoring, control decision-making 27

 adult survey, pheromone-baited traps, hypothetical damage prediciton 29

 adult survey, pheromone-baited traps, control decision-making 32

Oak Leaftier, Croesia semipurpurana (Kearfott) Freeman 192

 egg survey, damage predictions 192

Spruce bud moth, Zeiraphera canadensis Mutuura & Freeman 194

 larval survey, sequential sampling plot 194

Spruce budworm, Choristoneura fumiferana (Clemens) 135, 142, 146, 148, 153, 156, 159, 162, 164,

 166

 comprehensive review of egg mass sampling methods 135

 damage survey, optimum sample sizes 142

 larval survey, whole branch from midcrown level 146

 comprehensive review of sampling methods of all life stages 148

 egg mass survey, sequential sampling plan 153

 original development of sampling methods 156

 larval survey, density per kg fresh weight method, statistical error corrections 159

 adult survey, pheromone-baited trap, monitoring population trends 162

 adult survey, pheromone-baited trap, monitoring when to start larval surveys 164

 egg mass and larval survey, aerial damage survey, damage predictions 166

Spruce seed moth, Cydia stobilella (L.)

 egg survey, control decision-making 249

Two-year-cycle spruce budworm, Choristoneura biennis Freeman 132

Western blackheaded budworm, Acleris gloverana (Walsingham) 125, 127, 129

 egg survey, hot water extraction technique 125
342

 larval survey, 3-tree sampling 127

 egg survey with known precision, hot water extraction technique, damage predictions 129

Western conifer seed bug, Barbara colfaxiana (Kearfott) 257

Western spruce budworm, Choristoneura occidentalis Freeman 170, 172, 174, 177, 179, 182, 184

 egg mass survey, monitoring population trends 170

 larval survey, sampling criteria 172

 larval survey, mid-crown branch tip estimates 174

 egg mass survey, branch tip estimate 177

 larval survey, midcrown estimates from lower crown samples, optimum sample sizes 179

 defoliation survey, damage predictions 180

 pupal survey with known precision 182

 larval survey, beat-sheet method, mid-crown estimate 184

Leptoglossus occidentalis Heidemann

 See Hemiptera: Coreidae, Western conifer seed bug

Liang, Q. 69

Liang, Q.; Otvos, I. S.; Bradfield, G. E. 66, 68

Liebhold, A.; Twardus, D.; Buonaccorsi, J. 87

Lindgren, B. S. 293, 294

Lindgren funnel traps 294

line transected method 284

loss estimate 253

Luck, R. F.; Dahlsten, D. L. 110

lure-baited trap 269

Lymantria dispar (L.)

 See Lepidoptera: Lymantriidae, Gypsy moth

Lynch, A. M.; Fowler, G. W.; Witter, J. A. 139

Lyytikäinen-Saarenmaa, P.; Anderbrant, O.; Löfqvist, J.; Hedenström, E.; Högberg, H.-E. 52

M

MacLean, D. A.; MacKinnon, W. W. 142

Malacosoma disstria (Hübner)

See Lepidoptera: Lasiocampidae, Forest tent caterpillar

Malinoski, M. K.; Paine, T. D. 27

Mason, R. R. 97, 102, 103, 105, 110

Mason, R. R.; Overton, W. S. 96

Mason, R. R.; Paul, H. G. 98

Mason, R. R.; Scott, D. W.; Paul, H. G. 104

Mason, R. R.; Wickman, B. E. 102

McCarthy, J.; Olson, C. E., Jr.; Witter, J. A. 167

McKnight, M. E.; Chansler, J. F.; Cahill, D. B.; Flake, H. W., Jr. 178

Megastimus spemotrophus (Wachtl)

See Hymenoptera: Torymideae, Western conifer seed bug

Melanaspis obscura (Comstock)

 See Hemiptera: Diaspidae, Obscure scale

mid-crown branch estimates from lower crown samples 179

mid-crown estimate 184

mid-crown estimates from lower crown samples 98

Miller, C. A.; Kettela, E. G.; McDougall, G. A. 161

Miller, G.E 259

Mindarus abietinus Koch

 See Hemiptera: Aphididae, Balsam twig aphid

miscellaneous insects 201–208
343

 indicator plant phenology, control decision-making 203

monitoring population trends 52, 104, 162, 170, 291, 292

monitoring population trends from nonoutbreak phase 96

monitoring when to start larval surveys 164

Montgomery, B. A.; Simmons, G. A.; Witter, J. A.; Flexner, J. L. 139, 148

Moody, B. H.; Otvos, I. S. 146

Morris, R. F. 153, 156, 171

Morris, R. F.; Miller, C. A. 158

Morse, B. W.; Kulman, H. M. 54

Mountain pine beetle

See Coleoptera: Curculionidae

Multiple seed and cone insects 260

 damaged cone and seed survey, optimal sample sizes 260

 damage cone and seed survey, optimal timing

 damaged cone and seed survey, cut cone surface method, harvest decision-making 262

Mussey, G. J.; Potter, D. A. 203

N

Nealis, V. G.; Lomic, P. V. 188, 191

Nealis, V. G.; Turnquist, R. 132

Negrón, J. F.; Schaupp, W. C.; Johnson, E. 289

Neodiprion abietis (Harris)

See Hymenoptera: Diprionidae. Balsam fir sawfly

Neodiprion sertifer (Geoffroy)

See Hymenoptera: Diprionidae, European pine sawfly

Neophasia menapia (Felder & Felder)

See Lepidoptera: Pieridae, Pine butterfly

O

Oak leaftier

See Lepidoptera: Tortricidae

Obscure scale

See Hemiptera: Diaspidae

Oligonychus ununguis (Jacobi)

See Acari: Tetranychidae, Spruce spider mite

Olson, C. E., Jr.; Sacks, P. J.; Witter, J. A.; Bergelin, L. A. 167

Operophtera bruceata (Hulst)

See Lepidoptera: Geometridae, Bruce spanworm

Operophtera brumata (L.)

 See Lepidoptera: Geometridae, Winter moth

optimum sample sizes 14, 20, 42, 142, 179, 190, 217, 239, 260

optimal timing 260

Orangestriped oakworm

See Lepidoptera: Saturniidae

Orgyia pseudotsugata (McDonnough)

Lepidoptera: Lymantriidae, Douglas fir tussock moth 91

original development of sampling methods 156

Otvos, I. S.; Bryant, D. G. 63, 73

oviposition trap 60, 62, 80, 82, 84

P
344

Pendrel, B. A. 3

pest management timeline 107

pheromone-baited traps 3, 23, 29, 32, 52, 54, 64, 74, 76, 78, 86, 89, 94, 111, 114, 162, 164

piercing and sucking insects and mites 209–246

 See also Acari: Tetranychidae

 Hemiptera: Adelgidae

 Hemiptera: Aphididae

 Hemiptera: Cercopidae

 Hemiptera: Diaspidae

 Hemiptera: Drepanosiphidae

 Hemiptera: Tingidae

Pikonema alaskensis (Rohwer)

See Hymenoptera: Tenthredinidae, Yellowheaded spruce sawfly

Pine Butterfly

See Lepidoptera: Pieridae

Pivnick, K. A.; Barton, D. L.; Millar, J. G.; Underhill, E. W. 76

population estimation

 dissected bolt method 277

 fixed bark sample 287

 nondestructive method 281

population estimation within stand 279

Potter, D. A.; Jensen, M. P.; Gordon, F. C. 237

preliminary damage survey on spruce 14

 optimum sample sizes 14

Pristiphora erichsonii (Hartig)

 See Hymenoptera: Tenthredinidae, Larch sawfly

processing lichen samples 72

processing moss and bark samples 70

purple box trap 271

purple prism trap 273

Pyrrhalta (=Xanthogaleruca) luteola (Müller)

 See Coleoptera: Chrysomelidae, Elm leaf beetle

R

Raffa, K. F.; Hall, D. J. 199

Raupp, M. J.; Davidson, J. A.; Koehler, C. S.; Sadof, C. S.; Reichelderfer, K. 119

Ravlin, F. W.; Bellinger, R. G.; Roberts, E. A. 88

Reding, M. E.; Klein, M. G.; Brazee, R. D.; Krause, C. R. 5

Red oak borer

See Coleoptera: Cerambycidae

Reeks, W. A. 84

Regan, R. P.; De Angelis, J. D.; Gredler, G. 25

Régnière, J.; Boulet, B.; Turgeon, J. J. 194

Régnière, J.; Lysyk, T. J.; Auger, M. 159

Reichenbacker, R. R.; Schultz, R. C.; Hart, E. R. 38, 41

review of typical phases of outbreak 102

Richmond, D. S.; Shetlar, D. J. 211

risk-rating susceptible stands 186

roadside sampling 66

Ruth, D. S.; Miller, G. E.; Sutherland, J. R. 249
345

S

Saddled prominent

 See Lepidoptera: Notodontidae

Safranyik, L.; Linton, D. A. 2, 284

sampling criteria 172

Sanders, C. J. 151, 162, 164

Saratoga spittlebug

See Hemiptera: Cercopidae

Schizolachnus piniradiatae (Davidson)

 See Hemiptera: Aphididae, Woolly pine needle aphid

Schmid, J. M. 1984 174

Schmid, J. M.; Farrar, P. A. 177

Schmidt, B. C.; Roland, J.; Wakarchuk, D. 86

second year damage predictions 132

seed and cone insects 247–264

 See also Diptera: Anthomyiidae

 Hemiptera: Coreidae

Seedling debarking weevil

 See Coleoptera: Curculionidae

sequential sampling plan 11, 16, 56, 84, 153, 194, 289

sequential sampling plan with known precision 215, 229

Sharov, A. A.; Liebhold, A. M. 89, 90

Sharov, A. A.; Liebhold, A. M.; Roberts, E. A. 89, 90

Sharov, A. A., Roberts, E. A.; Liebhold, A. M.; Ravlin, F. W. 90

Shepherd, R. F. 180

Shepherd, R. F.; Gray, T. G. 70, 111, 129

Shepherd, R. F.; Gray, T. G.; Maher, T. F. 111

Shepherd, R. F.; Otvos, I. S. 107

Shepherd, R. F; Otvos, I. S.; Chorney, R. J. 107

Shepherd, R. R 103

Shore, T. 288

Shore, T. L. 63, 72, 179

Shore, T. L.; Alfaro, R. I. 179

Shrewsbury, P. M.; Hardin, M. R. 213

Silver, G. T. 130

Smith, C. C. 17

Southwood, T. R. E. 21, 71

Sower, L. L.; Daterman, G. E.; Stelzer, M. J.; Nielsen, D. G. 109

Spear-O’Mara, J.; Allen, D. C. 114

Spiral spruce cone borer

See Diptera: Anthomyiidae

Spruce beetle

See Coleoptera: Curculionidae

Spruce bud moth

 See Lepidoptera: Tortricidae

Spruce budworm

 See Lepidoptera: Tortricidae

Spruce seed moth

See Diptera: Anthomyiidae
346

Spruce spider mite

 See Acari: Tetranychidae

Srivastava, N.; Beckwith, R. C.; Campbell, R. W.; Torgersen, T. R. 182

Starkey, D.; Mangini, S.; Oliveria, F.; Clarke, S.; Bruce, B.; Kertz, R.; Menard, R. 282

statistical error corrections 159

Steel, R. G. D.; Torrie, J. H. 218

Strobilomyia neanthracina (Michelsen)

 See Diptera: Anthomyiidae, Spiral spruce cone borer

Strong, W. B.; Bates, S. L.; Stoehr, M. U. 256

Struble, G. R. 51

Surgeoner, G. A.; Wallner, W. E. 116

Sycamore lace bug

See Hemiptera: Tingidae

T

Thompson, M. G. 73

Thrips calcaratus Uzel

 See Thysanoptera: Thripidae, Introduced basswood thrips

Thyridopteryx ephemeraeformis (Haworth)

 See Lepidoptera: Psychidae, Bagworm

Thysanoptera: Thripidae

Introduced basswood thrips, Thrips calcaratus Uzel 198

 comparison of three different survey methods 198

timed visual method 109

timed walk method 87

Torgersen, T. R.; Scott, D. W.; Gillespie, A. J. R.; Hosman, K. P. 184

Trial, H., Jr.; Devine, M. E. 167

Turgeon, J. J.; Régnière, J. 195

Two-year-cycle spruce budworm

See Lepidoptera: Tortricidae

U

Underhill, E. W.; Millar, J. G.; Ring, R. A.; Wong, J. W.; Barton, D.; Giblin, M. 77, 78

Unruh, T. R.; Luck, R. F. 20

U.S. Department of Agriculture, Forest Service 282

V

Variable oakleaf caterpillar

Lepidoptera: Notodontidae, Heterocampa manteo (Doubleday) 116

Volney, W. J. A. 190

W

Webster, R. 215, 229

Werner, S. M.; Nordheim, E. V.; Raffa, K. F. 198

Western blackheaded budworm

See Lepidoptera: Tortricidae

Western conifer seed bug

See Hemiptera: Coreidae; See Hymenoptera: Torymidae

Western hemlock looper
347

See Lepidoptera: Geometridae

Western spruce budworm

See LepidopteraL Lymantriidae; See Lepidoptera: Tortricidae

Williamson, R. C 7

Wilson, L. F. 234

Wilson, R. W. Jr.; Fontaine, G. A. 88

Winjum, J. K.; Johnson, N. E. 263

Winter moth

See Lepidoptera: Geometridae

Witter, J. A.; Lynch, A. M. 166

wood- and bark-boring insects 265–294

 See also Coleoptera: Buprestidae

 Coleoptera: Cerambycidae

 Coleoptera: Curculionidae 

Woolly pine needle aphid

See Hemiptera: Aphididae

Wulder, M. A.; Dymond, C. C.; Erickson, B. 287

Y

Yellowheaded spruce sawfly

See Hymenoptera: Tenthredinidae

Z

Zeiraphera canadensis Mutuura & Freeman

See Lepidoptera: Tortricidae, Spruce bud moth