Sampling Methods

Forest Health

Technology Enterprise Team

TECHNOLOGY

TRANSFER

Sampling Methods for Forest and Shade Tree 

Insects of North America Volume 1

Christopher J. Fettig

Jeffrey G. Fidgen

Quintin C. McClellan

Scott M. Salom

Forest 

Service

FHTET-2001-01

2nd Edition - April 2010

United States

Department of

Agriculture

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

Printed on recycled paper.

References to pesticides appear in this publication. Publication of these 

statements does not constitute endorsement or recommendation of 

them by the U. S. Department of Agriculture, nor does it imply that uses 

discussed have been registered. Use of most pesticides is regulated by 

state and federal laws. Applicable regulations must be obtained from the 

appropriate regulatory agency prior to their use.

CAUTION: Pesticides can be injurious to humans, domestic animals, desirable 

plants, and fish and other wildlife if they are not handled and applied properly. 

Use all pesticides selectively and carefully. Follow recommended practices given 

on the label for use and disposal of pesticides and pesticide containers.

The use of trade, firm, or corporation names in this publication is for the information only 

and does not constitute an endorsement by the U.S. Department of Agriculture.

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.

http://www.fs.fed.us/foresthealth/technology/

The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs 

and activities on the basis of race, color, national origin, sex, religion, age, disability, 

political beliefs, sexual orientation, or marital or family status. (Not all prohibited 

bases apply to all programs.) Persons with disabilities who require alternative 

means for communication of program information (Braille, large print, audiotape, 

etc.) should contact USDA’s TARGET Center at (202) 720-2600 (voice and TDD). 

To file a complaint of discrimination, write to USDA, Director, Office of Civil 

Rights, Room 326-W, Whitten Building, 1400 Independence Avenue, S.W., 

Washington, D.C. 20250-9410, or call (202) 720-5964 (voice and TDD). 

USDA is an equal opportunity provider and employer.

CAUTION:

PESTICIDES

We are grateful to Warren Mays, J. Wilker and Kelly Wilson of Virginia Polytechnic and State University for support in locating journal articles and L. Fidgen for for reviewing earlier versions of our seed and cone reviews. In addition, we thank Dr. 

John Foltz of the University of Florida for his comments on an earlier version of this manuscript. We also acknowledge the 

many soursces that permitted us to reproduce original illustrations. This review was supported in part by funding to Scott M. 

Salom by the USDA Forest Service, Forest Health Technology Enterprise Team (FHTET)., the Hyland R. Johns Grant from 

the International Society of Arboriculture Research Trust, and a CALS and Distance Education Grant from Virginia Tech 

University. We also thank Denise Binion for editing the manuscript and FHTET for prividing funds for printing.

For additional copies of this publication contact Scott Salom at (540) 231-2794 (salom@vt.edu) or Richard Reardon 

at (304) 285-1566 (rreardon@fs.fed.us)
Sampling Methods for Forest and Shade 

Tree Insects of North America Volume 1

Christopher J. Fettig, Jeffrey G. Fidgen, 

Quintin C. McClellan, and Scott M. Salom

i

Contents

Preface -------------------------------------------------------- vii

Introduction ------------------------------------------------- 1

Using This Publication----------------------------------------------2 

References-------------------------------------------------------------2

Bud, Shoot, and Root Insects

Coleoptera: Curculionidae------------------------------------------5

Pales Weevil, Hylobius pales (Herbst)

Adult survey, forecasting seedling damage, pit trap method........................................5

Adult survey, PVC pitfall trap method ............................................................................7

Adult survey, PVC pitfall trap method ............................................................................8

Adult survey, damage prediction, PVC pitfall trap method..........................................9

Pine Root Collar Weevil, Hylobius radicis Buchanan

Adult survey, damage prediction, PVC pitfall trap method........................................11

Comprehensive review of survey techniques, hazard rating systems.......................12

Pitcheating Weevil, Pachylobius picivorus (Germar)

Adult survey, PVC pitfall trap method ..........................................................................15

Adult survey, PVC pitfall trap method ..........................................................................16

Adult survey, damage prediction, PVC pitfall trap method........................................17
ii

Coleoptera: Scarabiidae ------------------------------------------- 19

White Grubs, Phyllophaga spp.

Larval survey, sequential sampling plan.........................................................................19

Lepidoptera: Hepialidae ------------------------------------------ 23

Conifer Swift Moth, Korscheltellus gracilis (Grote)

Adult survey, sticky and Malaise trap methods.............................................................23

Lepidoptera: Tortricidae ------------------------------------------ 25

Western Pine Shoot Borer, Eucosma sonomana Kearfott

Visual, shoot dissection and pheromone-baited trap methods..................................25

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

Regression equation relating tree damage to moth density ........................................27

Nantucket Pine Tip Moth, Rhyacionia frustrana (Comstock)

Visual field damage estimates, regression equations....................................................28

Larval survey, absolute population estimates, FORTRAN program........................29

Sampling with known precision, FORTRAN program..............................................30

Sequential sampling plan..................................................................................................32

Pitch Pine Tip Moth, Rhyacionia rigidana (Fernald)

Larval survey, absolute population estimates, FORTRAN program........................36

Sampling with known precision, FORTRAN program..............................................37

Spruce Budmoth, Zeiraphera canadensis Mutuura and Freeman

Egg and larval surveys, sequential sampling plan for larvae ......................................39

Defoliating Insects

Coleoptera: Chrysomelidae--------------------------------------- 43

Elm Leaf Beetle, Pyrrhalta luteola (Müller)

Egg mass survey, control decision-making ...................................................................43

Hymenoptera: Diprionidae--------------------------------------- 45

Redheaded Pine Sawfly, Neodiprion lecontei (Fitch)

Comprehensive review of risk, detection and evaluation surveys.............................45

Red Pine Sawfly, Neodiprion nanulus nanulus Schedl.

Egg survey, sequential sampling plan.............................................................................49

European Pine Sawfly, Neodiprion sertifer (Geoffroy)

Egg survey, use of vertical sampling pole to determine egg density ........................51

Larval survey, regression equation relating damage to sawfly density ......................52

Swaine Jack Pine Sawfly, Neodiprion swainei Middleton

Egg survey, sequential sampling plan.............................................................................53

Hemlock Sawfly, Neodiprion tsugae Middleton

Egg survey, sequential sampling plan.............................................................................55

Lepidoptera: Coleophoridae ------------------------------------- 57

Larch Casebearer, Coleophora laricella (Hübner)

Over-wintering larval surveys, damage prediction.......................................................57

Lepidoptera: Gelechiidae ----------------------------------------- 59

Lodgepole Needle Miner, Coleotechnites milleri (Busck)

Larval survey, sequential sampling plan.........................................................................59

Larval and pupal survey, sequential sampling plan ......................................................61
iii

Lepidoptera: Geometridae --------------------------------------- 63

Eastern Hemlock Looper, Lambdina fiscellaria fiscellaria (Guenée)

Egg survey, sequential sampling plan.............................................................................63

Method for processing moss and bark samples for egg sampling ............................65

Western Hemlock Looper, Lambdina fiscellaria lugubrosa (Hulst)

Egg distributions within trees, common sampling problems.....................................66

Egg sampling with known precision, optimum sample sizes.....................................67

Pupal sampling with known precision, burlap band method.....................................69

Bruce Spanworm, Operophtera bruceata (Hulst)

Egg survey, ABS pipe oviposition trap method...........................................................71

Lepidoptera: Lasiocampidae ------------------------------------- 72

Forest Tent Caterpillar, Malacosoma disstria (Hübner)

Egg mass sampling, regression equations for density .................................................72

Egg survey, sequential sampling plan.............................................................................73

Egg survey, sequential sampling plan adjusted for stage of outbreak......................75

Lepidoptera: Lymantriidae --------------------------------------- 78

 Gypsy Moth, Lymantria dispar (L.)

Egg mass survey, 100-tree plot method.........................................................................79

Egg mass survey, adjusting winter estimates.................................................................81

Egg mass survey, fixed- and -variable radius plot methods........................................82

Egg mass survey, sequential sampling and fixed-radius plots....................................84

Egg mass survey, sequential sampling forested and suburban areas.........................86

Egg mass survey, two 100-m2

 plots to predict defoliation levels...............................90

Egg mass survey, constant-precision levels...................................................................91

Frass measurement, pellet numbers and size................................................................93

Egg mass survey, one 100-m2

 plot to predict defoliation ...........................................95

Egg mass and pupal survey, damage prediction 10 km from site ............................98

Regression equations for defoliation prediction in 1 hectare grids...........................99

Egg mass survey, suburban environments..................................................................101

Egg mass survey, burlap band method........................................................................103

Egg mass survey, fixed- and -variable radius plot methods......................................104

Douglas-Fir Tussock Moth, Orygia pseudotsugata (McDunnough)

Comprehensive review of general sampling methods..............................................105

Egg and larval survey, sequential sampling plan ........................................................106

Egg and larval sampling with known precision..........................................................109

Larval survey, low density populations, regression equations..................................111

Larval survey, sequential sampling plan.......................................................................114

Comprehensive review of larval sampling methods .................................................116

Mid-crown population estimates from lower crown samples..................................117

Egg mass survey, sequential sampling plan.................................................................119

Larval survey, sequential sampling plan.......................................................................121

Adult survey, pheromone-baited traps, crude damage predictions.........................123

Egg mass survey, crude defoliation predictions.........................................................124
Lepidoptera: Notodontidae -------------------------------------126

Saddled Prominent, Heterocampa guttivitta (Walker)

Egg and larval survey, sequential sampling plan ........................................................126

Lepidoptera: Saturniidae-----------------------------------------128

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

Defoliation surveys, control decision-making ............................................................128

Egg and larval surveys with known precision ............................................................129

Pandora Moth, Coloradia pandora Blake

Larval survey, operational sampling with known precision......................................131

Lepidoptera: Tortricidae -----------------------------------------132

Two-Year Cycle Spruce Budworm, Choristoneura biennis Freeman

Egg mass survey, egg deposition distributions within trees.....................................132

Spruce Budworm, Choristoneura fumiferana (Clemens)

Review of survey, impact and hazard rating methods...............................................133

Adult survey, pheromone-baited trap method ...........................................................135

Larval survey with known precision, optimum sample sizes...................................136

Egg mass survey, egg deposition distributions within trees.....................................138

Egg mass survey, sequential sampling plans...............................................................139

Larval survey with known precision, optimum sample sizes...................................140

Review of sampling methods for all life stages, hazard rating.................................142

Egg mass survey, mid-crown estimates.......................................................................151

Larval survey, sequential sampling plan.......................................................................152

Egg mass survey, branch “tip” estimates.....................................................................154

Western Spruce Budworm, Choristoneura occidentalis Freeman

Egg mass and larval sampling, regression equations.................................................157

Larval sampling, sequential sampling plan..................................................................159

Egg mass surveys, egg deposition distributions within trees...................................162

Larval survey with known precision, non-destructive sampling..............................163

Egg mass survey, sequential sampling plan, damage prediction..............................165

Visual and cut-branch methods of classifying defoliation levels.............................169

Egg mass, larval and pupal surveys, regression equations........................................170

Review of sampling methods for all life stages..........................................................171

Jack Pine Budworm, Choristoneura pinus Freeman

Stand density influences on egg mass density ............................................................176

Larval and pupal survey, regression equations...........................................................177

Larval survey, regression equations..............................................................................178

Larval survey, optimum sample sizes, regression equations.....................................179

Adult survey, pheromone-baited trap method ...........................................................181

Piercing and Sucking Insects and Mites

Acari: Tetranychidae----------------------------------------------183

Hemlock Rust Mite, Nalepella tsugifolia Keifer

Scouting program, control decision-making...............................................................183

Spruce Spider Mite, Oligonychus ununguis (Jacobi)

Sequential sampling plan, control decision-making...................................................184

iv
v

Homoptera: Adelgidae -------------------------------------------186

Eastern Spruce Gall Adelgid, Adelges abietis (L.)

Gall survey, distribution within trees ...........................................................................186

Cooley Spruce Gall Adelgid, Adelges cooleyi (Gillette)

Comparison of four different survey methods..........................................................187

Balsam Woolly Adelgid, Adelges piceae (Ratzeburg)

Estimating absolute population density.......................................................................188

Pine Leaf Adelgid, Pineus pinifolia (Fitch)

Sequential sampling plans..............................................................................................189

Homoptera: Aphididae-------------------------------------------194

Painted Maple Aphid, Drepanaphis acerifoliae (Thomas)

Population monitoring, crude control decision-making ...........................................194

Balsam Twig Aphid, Mindarus abietinus Koch

Beat disc and visual count surveys, crude control decision-making .......................195

Homoptera: Cercopidae -----------------------------------------196

Saratoga Spittlebug, Aphrophora saratogensis (Fitch)

Review of survey methods, control decision-making ...............................................196

Homoptera: Coccidae --------------------------------------------199

Citricola Scale, Coccus pseudomagnoliarum (Kuwana)

Population monitoring, crude control decision-making ...........................................199

Seed and Cone Insects

Diptera: Anthomyiidae-------------------------------------------201

Spruce Cone Maggot, Strobilomyia neanthracina Michelsen

Egg and larval survey, crude control decision-making..............................................201

Egg and larval survey, control decision-making.........................................................202

Diptera: Cecidomyiidae ------------------------------------------204

Douglas-Fir Cone Gall Midge, Contarinia oregonensis Foote

Egg survey, egg deposition distribution, recommended sample sizes....................204

Fixed and sequential sampling plans, tree and seed orchard level...........................205

Lepidoptera: Tortricidae -----------------------------------------209

Douglas-Fir Cone Moth, Barbara colfaxiana (Kearfott)

Egg survey, sequential sampling plan...........................................................................209

Spruce Seed Moth, Cydia strobilella (L.)

Egg and larval survey, crude control decision-making..............................................211

Seed and Cone Insects -------------------------------------------212

Multiple species in southern pine seed orchards

Survey methods, estimate yield and damage, computer program ...........................212

Coleoptera: Cerambycidae---------------------------------------215

Northeastern Sawyer, Monochamus notatus (Drury)

Damage survey, sequential sampling plan ...................................................................215

Oregon Fir Sawyer, Monochamus scutellatus oregonensis (LeConte)

Damage survey, sequential sampling plan ...................................................................217
vi

Wood- and Bark-Boring Insects

Coleoptera: Cerambycidae---------------------------------------215

Northern Sawyer, Monochamus notatus (Drury)

Damage survey, sequential sampling plan ..................................................................215

Oregon Fir Sawyer, Monochamus scutellatus oregonensis (LeConte)

Damage survey, sequential sampling plan ...................................................................217

Coleoptera: Scolytidae--------------------------------------------218

Western Pine Beetle, Dendroctonus brevicomis LeConte

Egg and larval survey with known precision, optimum sample size ......................218

Southern Pine Beetle, Dendroctonus frontalis Zimmermann

Forecasting of regional infestation trends, pheromone-baited traps......................220

Arkansas SPBMODEL, predicting spot growth........................................................223

Estimation of attacking adult densities by three methods .......................................225

Estimation of within-tree population density ............................................................227

Estimation of stand level population density.............................................................229

Review of aerial detection, ground checking, and hazard rating.............................231

Mountain Pine Beetle, Dendroctonus ponderosae Hopkins

Estimation of within-tree density, optimum sample sizes........................................234

Adult survey, within-tree populations, sequential sampling plan.............................236

Spruce Beetle, Dendroctonus rufipennis Kirby

Sequential sampling plan, damage prediction.............................................................239

Fir Engraver, Scolytus ventralis LeConte

General survey of within-tree populations ................................................................242

Striped Ambrosia Beetle, Trypodendron lineatum (Olivier)

Adult survey, spatial and temporal distributions on log sorts..................................244

Glossary-----------------------------------------------------247

References -------------------------------------------------249

Permissions Acknowledgments-------------259

Index ----------------------------------------------------------261
Preface

I 

n a shade tree pest management course taught at Virginia Polytechnic Institute and State University, one 

of us (Scott M. Salom) was explaining the importance of sampling as an essential component of any 

integrated pest management (IPM) program. This explanation was followed by a presentation of some 

of the more common examples of sampling methods used for estimating insect populations. A student 

then asked if there was access to a compilation of this information for all forest and shade tree insect 

pests. The response was “no,” that information for individual pests is in the scientific literature, much of 

it inaccessible to the average forester or arborist, either logistically or through the technical or scientific 

jargon used in the article that is likely unfamiliar to the general practitioner.

This exchange demonstrated a need to bring together all the information available on sampling methods 

for forest and shade tree insects, and to make it accessible in an unambiguous and comprehensive 

manner. The objectives of compiling this publication were to provide a valuable tool for natural resource 

professionals who are involved in pest management decision-making, and to identify key forest pests for 

which this type of information is lacking, and for which further research is warranted. 

Although we searched the literature for three years, we inevitably were unable to locate all sampling 

papers, and perhaps missed others entirely. To fill these gaps, we are simultaneously developing a web site 

that will allow users to search for sampling procedures for individual pests. Our hope is to continually 

update the web site to provide a current resource detailing available sampling methods for forest insect 

pests (www. everest.ento.edu/~salom/Samplemeth). 

We are pleased to present the results of our efforts, and hope you will find this text a useful resource for 

managing forest and shade tree insect populations.

Christoper J. Fettig 

Jeffrey G. Fidgen 

Quintin C. McClellan 

Scott M. Salom

vii

INTRODUCTION

1

Introduction

The forester or arborist that suspects significant pest activity may attempt to diagnose the problem by 

identifying the causal agent or its damage. According to integrated pest management principles, the mere 

presence of a pest does not warrant control. The population density of the pest or level of damage has 

to reach or exceed an action threshold before treatment is justified. This approach greatly reduces the 

amount of insecticide used and the cost of applying it or other tactics. However, a sampling plan must 

be developed to help individuals establish action thresholds and make accurate and judicious control 

decisions.

Management tools are constantly being developed for forest and shade tree pests; however, much of 

this information is scattered throughout the literature. Textbooks (Southwood 1978, Pedigo and Buntin 

1994) and review articles (Strickland 1961, Kuno 1991) describe the theory and application of sampling 

techniques for estimating insect population densities and describing population distributions within a 

host habitat. Book chapters focus on types of sampling surveys used for forest and shade tree pests 

(Coulson and Witter 1984, Barbosa and Wagner 1989). Journal articles introduce the development of 

new sampling methods for estimating population densities and monitoring trends of individual species 

(Coffelt and Schultz 1994, Gargiullo and others 1983, Mason 1987, Stark 1952). Extension publications 

and conference proceedings often provide a how-to approach for individual sampling procedures that are 

used in pest management decision-making (Billings 1988, Fleischer and others 1992, McGraw and Hain 

1979).

To compile the existing information on sampling procedures for forest and shade tree insect pests of 

North America, we conducted an exhaustive library search that yielded over 300 publications. Of these, 

we considered 123 appropriate for inclusion here, using the criterion that the publication had to describe 

a sampling procedure for use in pest management decision-making. This publication contains reviews of 

those 123 papers.
2

Using This Publication

The papers reviewed in this report have been categorized by five insect feeding groups:

Bud, shoot, and root insects

Defoliating insects

Piercing and sucking insects and mites

Seed and cone insects

Wood- and bark-boring insects

Within each group, the species are organized in alphabetical order, by order, family, and genus, with the 

common name listed first. For an individual species, a review of one or several original publications may 

be provided. Each review usually contains a citation, objective, abstract, sampling procedure, tables or 

figures, notes, and references. The objective provides a brief description of the objectives stated in the 

original publication, and describes the reasons why the study was conducted. The sampling methods used 

may not be explicitly stated in the objective if they simply served as a tool to achieve other objectives. 

The abstract provides a brief description of the pest, but focuses on the data collected and development 

of the sampling procedure in the original publication. The sampling procedure provides a detailed stepby-step description of how to employ the sampling method within a given habitat. Tables or figures 

may be included to help illustrate the procedure, and have either been taken directly from the original 

publication or redrawn. The original table and figure numbers have been retained for ease of comparison 

should you choose to consult the original publication. Notes may be included to put the paper in 

perspective in relation to other publications or to offer caveats concerning appropriate use or limitations 

of a procedure. When applicable, a references section completes the review. References beginning with 

an asterisk (*) are reviewed in this publication, and can be located using the alphabetical index at the end 

of this text. The publication concludes with a brief glossary of forestry and entomological terms that 

might be useful. If you encounter difficulties understanding or using certain sampling techniques, we 

recommend you refer to the original publication for further clarification.

References

Barbosa, P.; Wagner, M. R. 1989. Introduction to forest and shade tree insects. San Diego: Academic Press, Inc.; 639 

p.

Billings, R. F. 1988. Forecasting southern pine beetle infestation trends with pheromone traps. In Payne, T. L.; 

Saarenmaa, H., editors. Integrated control of Scolytid bark beetles: proceedings of IUFRO Working Party 

and International Congress of Entomology Symposium; 1988 July 4; Vancouver, BC, Canada; 295-306. 

Coffelt, M. A.; Schultz, P. B. 1994. Within-tree distribution and a fixed-precision-level sampling plan for the 

orangestriped oakworm (Lepidoptera: Saturniidae). Journal of Economic Entomology 87: 382-388.

Coulson, R. N.; Witter, J. A. 1984. Forest entomology—ecology and management. New York: John Wiley and Sons 

Inc.; 669 p. 

Fleischer, S.; 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. 

Gargiullo, P. M.; Berisford, C. W.; Pienaar, L. V. 1983. Two-stage cluster sampling for pine tip moths. Environmental 

Entomology 12: 81-90.
3

Kuno, E. 1991. Sampling and analysis of insect populations. Annual Review of Entomology 36: 285-304. 

Mason, R. R. 1987. Frequency sampling to predict densities in sparse populations for the Douglas-fir tussock moth. 

Forest Science 33: 145-156.

McGraw, J. R.; Hain, F. P. 1979. Spruce spider mite sampling system. Forest Research Notes. North Carolina State 

University; 6 p. 

Pedigo, L. P.; Buntin, G. D. 1994. Handbook of sampling methods for arthropods in agriculture. Boca Raton, FL: 

CRC Press Co.; 714 p.

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

York: Chapman & Hall Corp.; 524 p.

Stark, R. W. 1952. Sequential sampling of the lodgepole needleminer, Evagora milleri. Journal of Economic 

Entomology 55: 491-494.

Strickland, A. H. 1961. Sampling crop pests and their hosts. Annual Review of Entomology 6: 201-220. 

BUD, SHOOT, AND ROOT INSECTS

5

Pales Weevil 

Hylobius pales (Herbst) 

Coleoptera: Curculionidae

Fettig, C. J.; Salom, S.M. 1998. Comparisons of two trapping methods to Hylobius pales

(Coleoptera: Curculionidae) in Virginia. Environmental Entomology 27: 572-577.

Objectives 

To determine if standard pit traps attract more H. pales than PVC traps; and if weevil density is related 

positively to weevil-induced seedling damage, thus providing a tool to predict future stand risks. 

Abstract

The pales weevil, Hylobius pales (Herbst), is a major regeneration pest of Christmas tree plantations in 

the eastern USA. The development of an effective, easily implemented sampling strategy to detect when 

serious infestations are imminent is highly desirable, but does not exist currently in Virginia. A study 

was conducted during 1994 and 1995 in white pine, Pinus strobus L., Christmas tree farms in Floyd and 

Montgomery Co., Virginia. Most trees were of harvestable age (5-8 yr), and had no history of insecticide 

application during the previous five years. Methods were evaluated for trapping walking H. pales and for 

their potential at forecasting seedling damage. 

Weevil gender, trap rotation and density of ground cover did not explain a significant proportion of the 

variation in trap catch. Standard pit traps baited with a white pine billet and 5:1 mixture of 95% ethanol: 

turpentine caught significantly more weevils than PVC traps baited only with a 5:1 mixture of 95% 

ethanol: turpentine. Trap catch was related positively to seedling damage and wound surface area (mm2

) in 

1995 (Y = -6.62 + 0.087X, R2

 = 0.98, P < 0.002; Y = -422.69 + 4.80X, R2 = 0.99, P = 0.011), but not in 

1994. Although catches of both trap types are related positively, PVC pitfall traps did not detect the large 

initial peak that occurred in early summer. In addition, early season catch trends varied between the two 

methods at a time when weevil monitoring would have the greatest implication on management decisions. 

For these reasons, the use of pit traps for monitoring weevil populations in Virginia is recommended.

Sampling Procedure 

Pit traps: One freshly-cut white pine billet, 8-12 cm in diameter and 30 cm long, is treated with a 

registered insecticide to prevent weevil escape. A shallow depression is made in the ground within 20 cm 

of a healthy tree and the billet is placed firmly within the shallow depression. A 25-ml vial containing 

a 5:1 ethanol: turpentine mixture (62.1% α-pinene, 20.1% camphene, 10.8% β-pinene, 2.6% limonene, 

1.8% benzene, and 1.3% 3-carene) (Klean Strip SD-81, W. M. Barr. Memphis, TN) is then placed adjacent 

to the billet. Cover the trap with a black tile and fresh white pine foliage to reduce desiccation. Place traps 

at a density of approximately 12 per hectare. Weevils should be collected and baits replaced biweekly.

PVC pitfall traps: The basic pitfall trap design has been described in detail by Rieske and Raffa (1993). 

The only pertinent difference is that the 5:1 ethanol: turpentine mixture is released from a single, 25-ml 

vial.
6

Notes 

The strong positive relationship between trap catch and seedling damage is based solely on four data 

points. Therefore, these results should be interpreted with caution.

Reference

*Rieske, L. K.; Raffa, K. F. 1993. Potential use of baited pitfall traps in monitoring pine root weevil, Hylobius pales, 

Pachylobius picivorus, and Hylobius radicis (Coleoptera: Curculionidae) populations and infestation levels. Journal of Economic Entomology 86: 475-485.
7

Pales Weevil 

Hylobius pales (Herbst) 

Coleoptera: Curculionidae 

Mangini, A.; Carlton, C.; Perry, R. W.; Hanula, J. L. 1994. Seed, cone, regeneration, and 

defoliating insects in forest ecosystem management. Gen. Tech. Rep. 50. U.S. 

Department of Agriculture, Forest Service; 154-161. 

Objective 

To determine if H. pales posed any significant threat to regeneration in Phase II treatment stands.

Abstract

The pales weevil, Hylobius pales (Herbst), is a major regeneration pest of Christmas tree and commercial 

pine plantations in the eastern USA. This pest has also been implicated as a vector of pathogenic tree 

fungi. As part of the Phase II Ecosystem Management Research conducted on the Ouachita and Ozark 

National Forests, the Arthropod and Microbial Communities Study Group completed a survey of H. 

pales. Pitfall traps baited with 2 ml of turpentine (52.5% α-pinene, 41.4% β-pinene, 2% β-phellandrene 

and 1.1% limonene) and 2 ml of 95% ethanol caught H. pales. Adequate sampling accuracy was obtained 

by establishing three transects of 10 traps each per site. Traps were spaced on 12-m intervals. 

Sampling Procedure 

Use one 2-ml vial of turpentine and one 2-ml vial of 95% ethanol in each PVC pitfall trap (for a 

description of the trap see Hunt and Raffa 1989). Remove all trap contents, clean and re-bait each trap 

weekly.

Reference

*Hunt, D. W. A.; Raffa, K. F. 1989. Attraction of Hylobius radicis and Pachylobius picivorus (Coleoptera: Curculionidae) 

to ethanol and turpentine in pitfall traps. Environmental Entomology 18: 351-355. 
8

Pales Weevil

Hylobius pales (Herbst) 

Coleoptera: Curculionidae

Raffa, K. F.; Hunt, D. W. A. 1988. Use of baited pitfall traps for monitoring pales weevil, 

Hylobius pales (Coleoptera: Curculionidae). Great Lakes Entomology 21: 123-125. 

Objective 

To determine if ethanol and turpentine baited pitfall traps are an effective tool for monitoring H. pales 

populations. 

Abstract

The pales weevil, Hylobius pales (Herbst), is a serious pest of young pine, Pinus spp., plantations 

throughout eastern North America. Pitfall traps baited with 95% ethanol and turpentine (52.5% 

α-pinene, 41.4% β-pinene, 2% β-phellandrene, 1.1% limonene, 0.9% camphene and 0.7% myrcene) were 

used to monitor H. pales populations in Wisconsin. This study was carried out in a 15-year-old plantation 

of Scots pine, Pinus sylvestris L., which was already suffering high mortality from the pine root collar 

weevil, Hylobius radicis Buchanan. Male and female H. pales were attracted equally to this bait combination. 

Neither component alone showed any attractiveness. Approximately 75% of all weevils were trapped 

during July, with the remainder collected in August. Future studies are aimed at estimating the relationship 

between trap catch and tree damage, mainly to facilitate the integrated pest management (IPM) of H. 

pales. 

Sampling Procedure

Use sections of 20 cm long by 10 cm diameter plastic PVC pipe (see Tilles and others 1986). Drill eight 

6-mm entrance holes around the circumference of each trap, 4 cm from the top. Coat the inside of 

each trap with liquid Teflon (DuPont de Nemours, Wilmington, DE, USA) to prevent H. pales escape. 

Use a combination of 95% ethanol and turpentine (Sunnyside Corp., Wheeling, IL, USA) as bait. Place 

each bait component separately in 12 by 35-mm (0.5 dram) glass vials. Drill two 2-mm holes below the 

entrance holes and attach a small wire through the 2-mm holes. Suspend each bait in the trap from this 

wire. Cap both ends of each trap, and drill two holes in the bottom cap to allow water drainage. Bury 

each trap in the ground so that the entrance holes are flush with the soil surface. Coat the above-ground 

portion of each trap with black paint in order to simulate a stump. Empty each trap and replace baits 

weekly.

Note

Space eight traps uniformly across the area to be treated.

Reference 

Tilles, D. A.; Sjodin, K.; Nordlander, G.; Eidman, H. H. 1986b. Synergism between ethanol and conifer host volatiles as attractants for the pine weevil, Hylobius abietis (L. ) (Coleoptera: Curculionidae). Journal of Economic Entomology 79: 970-973.
9

Pales Weevil

Hylobius pales (Herbst) 

Coleoptera: Curculionidae

Rieske, L. K; Raffa, K. F. 1993. Potential use of baited pitfall traps in monitoring 

pine root weevil, Hylobius pales, Pachylobius picivorus, and Hylobius radicis

(Coleoptera: Curculionidae) populations and infestation levels. Journal of 

Economic Entomology 86: 475-485. 

Objective 

To determine the predictive potential of this monitoring system for use in an Integrated Pest 

Management (IPM) program for H. pales.

Abstract

Plantation pine, Pinus spp., production in the Lake States is often limited by the feeding of adult pales 

weevil, Hylobius pales (Herbst), which can cause extensive seedling mortality and disfigurement of young 

trees. Population fluctuations of H. pales were monitored in five Wisconsin Christmas tree plantations 

of 5-year-old Scots pine, Pinus sylvestris (L.). Trees were spaced 1.8 m apart, and had not received 

insecticide treatment. Pitfall traps were baited with 2 ml of turpentine (46% α-pinene, 42% β-pinene, 2% 

β-phellandrene, 1% limonene, 0.9% camphene and 0.8% myrcene) and 2 ml of 95% ethanol in separate 

vials and tested at 6, 18 and 32 traps per 432 m2

 to determine their ability to detect weevil activity.

The lowest trap density (6 traps) had the best correlation between trap catch and damage indices, and was 

the least expensive sample to obtain. The 1988 trap catch of female H. pales was correlated positively with 

infestation level and infestation severity in 1988, 1989 and 1990. This trapping method could be used to 

forecast H. pales density and predict damage, but more work is needed to confirm these relationships.

Sampling Procedure 

Pitfall traps: Use 17 cm long by 10 cm wide sections of plastic PVC pipe (see Tilles and others 1986). 

Drill eight 7-mm entrance holes around the perimeter of each trap, 6 cm from the top end. Coat the 

inside of each trap with liquid Teflon (DuPont de Nemours, Wilmington, DE, USA), which will prevent 

weevil escape. Drill an additional two 2-mm holes in the trap wall and attach a 14-gauge wire. Place a 2 

ml vial of 95% ethanol and a 2 ml vial of turpentine (Mantz Paint, Madison, WI, USA) in each trap by 

suspending each from the 14-gauge wire. The vials will then be approximately 4 cm below ground level. 

Cap both ends of the trap, and drill two 2-mm holes in the bottom cap to allow water drainage. Coat the 

aboveground cap and the exposed portion of the PVC pipe with flat black paint in order to resemble a 

tree trunk. Bury each trap until the entrance holes are flush with the soil surface level.

Space traps evenly within a 432 m2

 block (i.e., 8-9 m). Monitor all traps weekly throughout the activity 

period of H. pales. Remove all weevils and replenish the baits during each sample collection. 

To estimate infestation levels and the condition of tree foliage, take a subsample of trees within the 432 

m2

 and look for the following:

Infestation levels: Examine the root collar of 12 trees per block for larval tunneling to a depth of 12 cm 
10

into the soil. Calculate two indices of infestation based on this data:

1. infestation level = # trees infested/# trees in subsample

2. infestation severity = (the sum of the proportion of damaged stem perimeters/# 

subsampled trees) x 100.

Incidence of foliar symptoms: Grade all trees in block on the basis of needle color. Trees with green 

(visibly healthy), yellow (intermediate degradation), and red or brown (dead) needles are given a 1, 2 and 

3, respectively. Use this data to determine four indices of foliar symptoms:

1. symptom level = proportion of symptomatic trees

2. foliar severity = sum of foliar grades/# trees in replicate

3. proportion of yellow trees

4. proportion of red trees

These indices will help predict population density as well as provide an indication of damage levels.

Note

Better damage estimates may be obtained if pitcheating weevil, Pachylobius picivorus (Germar), pine root 

collar weevil, Hylobius radicis Buchanan, and H. pales are treated as a complex rather than trying to estimate 

damage separately for each individual species. 

Reference

Tilles, D. A.; Sjödin, K.; Nordlander, G.; Eidmann, H. H. 1986. Synergism between ethanol and conifer host volatiles as attractants for the pine weevil, Hylobius abietis (L.) (Coleoptera: Curculionidae). Journal of Economic Entomology 79: 970-973. 
11

Pine Root Collar Weevil

Hylobius radicis Buchanan 

Coleoptera: Curculionidae

Hunt, D. W. A.; Raffa, K. F. 1989. Attraction of Hylobius radicis and Pachylobius 

picivorus (Coleoptera: Curculionidae) to ethanol and turpentine in pitfall traps. 

Environmental Entomology 18: 351-355.

Objectives

To determine if H. radicis preferentially selects pitfall traps baited with red pine, Pinus resinosa Aiton, 

stems, turpentine, ethanol, or a combination of these baits; and if the number of H. radicis caught in 

traps is related positively to host damage.

Abstract

The pine root collar weevil, Hylobius radicis Buchanan, is an important pine, Pinus spp., pest in the 

northeastern USA and southeastern Canada. Larvae develop in the root collars of living hosts, which 

severely weakens and sometimes kills the host. The relative attractiveness of pitfall traps baited with red 

pine shoots, turpentine (52.5% α-pinene, 41.4% β-pinene, 2% β-phellandrene and 1.1% limonene), 

95% ethanol, or a combination of these baits, was tested for capturing adult H. radicis. This study was 

conducted in five Wisconsin Christmas tree plantations of 5-year old Scots pine, Pinus sylvestris L.

Hylobius radicis adults were trapped from May through August 1987. Traps baited with a 2-ml vial of 

turpentine (Sunnyside Corp., Wheeling, IL, USA), and a 2-ml vial of ethanol, at release rates of 200 mg 

and 40 mg per day (at 22o

C), respectively, were most effective for catching H. radicis. The sex ratio (% 

males) of H. radicis caught in the traps was 10%, suggesting that this bait combination is highly attractive 

to females. The number of H. radicis (Y) captured in pitfall traps was related positively to foliar damage 

(X) (Y = 3.2 + 3.4X; R2 = 0.87, P < 0.05). 

Sampling Procedure

Use 10 cm wide and 20 cm long sections of PVC pipe. Drill eight 6-mm entrance holes, at 4 cm 

intervals, around the circumference of each trap 4 cm from the top. Apply Fluon (DuPont de Nemours, 

Wilmington, DE, USA) to the inside walls of each trap to prevent weevil escape. Drill two 2-mm holes 

just below ground level and attach a small wire through the holes. Attach each of the trap baits to this 

wire. Place a cap on the ends of each trap, and drill two 2-mm holes in the bottom cap to permit water 

drainage. Bury each trap 16 cm deep so that the entrance holes are flush with the soil surface. Paint the 

upper cap, and exposed portion of each trap, black in order to resemble the silhouette of a stump. Traps 

were positioned within tree rows midway between every second tree, for a trap spacing of 3.4 m. Plots 

were at least 30 m from adjacent plots and the edge of the plantation.

Notes

Forty traps were used for each treatment in this study. No information is provided on the minimum 

number of traps needed to monitor weevil populations.
12

Pine Root Collar Weevil

Hylobius radicis Buchanan 

Coleoptera: Curculionidae

Wilson, L. F.; Millers, I. 1983. Pine root collar weevil—its ecology and management. 

Tech. Bull. No. 1675. Washington, DC: U.S. Department of Agriculture, Forest 

Service; 34 p.

Objectives

To provide a comprehensive review of the survey methods used to detect the presence and population 

density of H. radicis as well as assess techniques used to determine damage caused by this forest pest.

Abstract

The pine root collar weevil, Hylobius radicis Buchanan, is an important pest of pine, Pinus spp., plantations 

in the northeastern USA. Larvae cause most of the damage as they feed below ground and bore into 

the inner bark. As they grow larger, H. radicis larvae also damage xylem tissues. Signs of infestation 

include pitch-soaked soil adjacent to the root collar of attacked trees, reduced shoot vigor, chlorosis 

and windthrow. Several types of surveys can be conducted by forest landowners to detect, evaluate, and 

predict weevil populations or damage. Three direct surveys that look for the presence of H. radicis and 

two indirect surveys that assess damage caused by its feeding were presented. A hazard-rating system was 

also presented to guide in the establishment of new pine plantations.

Sampling Procedure 

The first three surveys deal with estimation of H. radicis presence and population levels whereas the last 

two surveys deal with estimation of damage.

Detection survey: First, look for obvious symptoms of weevil damage including yellowing or red foliage, 

or windthrown trees typical of heavy infestations. Second, examine the root collars of damaged trees for 

girdling by pulling the soil away with a shovel to look for blackened, pitch-soaked soil sticking to the bark. 

If this sign is present, remove more soil out to 15 cm from the root collar and look for larvae or pupae in 

the outer bark tissues. Stop sampling once H. radicis is found or continue with an appraisal survey.

Immature weevil appraisal survey: This survey determines the proportion of trees with immature weevils 

and should be conducted in stands with trees 5-13 cm diameter at ground level or trees 1-5 m in height. 

Sampling for immature weevils should be carried out between mid-June and mid-July when the greatest 

number of large immature weevils are present. Identify these specimens to confirm they are H. radicis.

Determine the parts of the stand that will be sampled and conduct the survey systematically. Only live, 

standing trees should be sampled. Use the following chart to determine the sampling intensity:

Sampling: Cut off one or more lower branches to expose the base of the tree. Remove the 

needle litter from the trunk out to about 30 cm. Dig a small trench around the tree 15 cm away 
13

from the trunk and 15 cm into the soil. Section the soil and examine each section by removing 

the soil and crumbling it to search for pitch and damage to the root collar. If there is no pitch 

present after digging around the tree, then record the tree as uninfested and move to the next 

tree. If pitch is present, then begin searching the soil for H. radicis larvae, pupae and callow adults 

(light- to reddish-brown weevils in pupal cells). Record the tree as either infested or uninfested 

based on the presence or absence of weevils, respectively, and move to the next tree. After 

sampling the minimum number of trees for the acreage in question, determine the proportion of 

infested trees. If the proportion of infested trees is >75%, then the infestation is severe enough 

to cause high mortality and control is warranted.

Adult appraisal survey: This survey determines the proportion of trees with adult H. radicis and should 

be conducted in stands with trees 5-13 cm in diameter at ground level or 1-5 m in height. The best time 

to conduct this survey is from mid-May through to the end of June when most adult weevils are close to 

the root collar for mating and oviposition. Be certain that any adult weevils found are H. radicis because 

several other weevil species may be found.

Determine the parts of the stand that will be sampled and conduct the survey systematically. Only live, 

standing trees should be sampled. See Table 1 for recommended sampling intensity. 

Stand size Sample trees

Acres Hectares Number per acre (0.4 ha) Total

 1 0.4 20 20

 3 1.2 7 21

 5 2.0 4 20

 10 4.0 3 30

>20 >8.0 2 40

Sampling: Cut off one or more lower branches to expose the base of the tree. Carefully search 

the interface between the organic layer and soil layer for adults, as well as the root collar out to 

about 46 cm. Be sure to examine bark crevices for hiding weevils. If one live H. radicis is found, 

then record the tree as infested and move to the next tree. If no weevils are found, then continue 

searching the soil around the root collar by digging down to a depth of 10 cm. If no weevils are 

found at this point, then record the tree as uninfested. After sampling the minimum number of 

trees for the acreage in question, determine the proportion of infested trees. If the proportion 

of infested trees is >40%, then the infestation is severe enough to cause tree mortality.

Damage appraisal survey: If tree mortality and windthrow have already occurred in the stand, then a 

nondestructive damage appraisal survey should be carried out. The number of freshly attacked trees is an 

accurate estimate of tree losses to occur the following year, assuming a rising or stable infestation. 

Sampling: Sample a cluster of trees every 20 m along transect lines throughout the stand. In each 

cluster, record the number of living and dead trees. Calculate the percentage of dead trees but 

make sure the dead trees were attacked and killed by H. radicis (see Detection Survey). If 3-5% of 

trees were recently killed, then approximately 95% of apparently healthy trees are likely infested.

Stand damage index: This survey is a destructive but detailed assessment of H. radicis damage that is used 

primarily for research purposes. This technique is also useful in practical field application when a precise 
14

estimate of damage is needed. Randomly select trees 1-3 m in height to be surveyed.

Sampling: Sample 20 to 30 trees systematically throughout the plantation or portion of the 

plantation to be surveyed. Dig around the root collar to root depth looking for larval injury. If 

no injury is found, then record the tree as uninfested. If injury is found, then cut the tree and 

remove the stump portion. Saw each stump in cross section across the area that appears to have 

the worst external damage. On one face of the cross section, calculate the degree of damage “d” 

by the formula: 

where Go’ Gi are measurements of girdled outer and inner bark (cm or inches) for each root collar; Co’ 

Ci are circumferences (cm or inches) for the outer and inner bark, and “n” represents the sample size. 

The damage index (DI) is calculated by multiplying the total damage (d), by the proportion of trees 

infested (p) and a constant (k) (i.e., DI = dpk, where k = 1,000). Three damage classes were developed:

1. Light - DI <100: Contains pines with <40% of the root collars scarred by larval feeding and 

no abnormal growth or off-color symptoms.

2. Moderate - 100<DI<300: Contains pines with 30-85% of the root collars scarred by larval 

feeding and >10% with shortened terminal growth.

3. Heavy - DI>301: Contains pines with 80-100% of the root collars scarred by larval feeding 

and from a few to many trees leaning, off-color, or dead.
15

Pitcheating Weevil

Pachylobius picivorus (Germar) 

Coleoptera: Curculionidae

Hunt, D. W. A.; Raffa, K. F. 1989. Attraction of Hylobius radicis and Pachylobius 

picivorus (Coleoptera: Curculionidae) to ethanol and turpentine in pitfall traps. 

Environmental Entomology 18: 351-355.

Objectives

To determine if P. picivorus preferentially selects red pine, Pinus resinosa (Aiton), stems, volatiles of 

turpentine and ethanol, or a combination of these baits, placed in pitfall traps; and if the number of P. 

picivorus caught in traps is related positively to host damage.

Abstract 

The pitcheating weevil, Pachylobius picivorus (Germar), feeds nocturnally on the inner bark of pine 

twigs, and can cause widespread mortality in newly planted stands. Pitfall traps were placed in a 5-yearold plantation of Scots pine, Pinus sylvestris (L.), in Waushara Co., Wisconsin, to evaluate the relative 

attractiveness of red pine stems, turpentine (52.5% α-pinene, 41.4% β-pinene, 2% β-phellandrene and 

1.1% limonene) (Sunnyside Corp., Wheeling, IL, USA) and 95% ethanol to adult weevils.

One trap per 72 m2

 was most effective at catching P. picivorus if baited in the spring with 2 ml each of 

ethanol and turpentine released from separate vials. Slightly more female than male P. picivorus were 

attracted to the traps. Because the peak activity period of H. radicis occurred earlier than P. picivorus, it 

is suspected that the initial wounding of trees by H. radicis attracts P. picivorus, thereby increasing risk to 

young host trees. No significant relationships were found between the number of P. picivorus caught in the 

pitfall traps and subsequent tree damage. However, the presence of high numbers of H. radicis, which is 

suspected to precondition tree hosts for P. picivorus, could be useful in defining damage thresholds.

Sampling Procedure

Use 10 cm wide and 20 cm long PVC pipe as the trap body. Drill eight 6-mm entrance holes at 4 

cm intervals 4 cm from the top of each trap. Apply liquid Teflon or Fluon (Dupont de Nemours, 

Wilmington, DE, USA) to the inside walls of each trap to prevent weevil escape. Drill two 2-mm holes 

into the trap wall, below the eight 6-mm holes and attach a small wire through the holes. Hang bait(s) 

from this wire. Place a plastic cap on both ends of the trap, and drill two 2-mm holes in the bottom 

cap to drain water. In each plot, bury eight traps horizontally until entrance holes are flush with the soil 

surface. Traps should be within tree rows midway between every second tree such that the trap spacing is 

3.4 m. Plots should be spaced 30 m apart. Empty the contents of each trap and replenish baits weekly.
16

Pitcheating Weevil

Pachylobius picivorus (Germar) 

Coleoptera: Curculionidae

Mangini, A.; Carlton, C.; Perry, R. W.; Hanula, J. L. 1994. Seed, cone, regeneration, and 

defoliating insects in forest ecosystem management. Gen. Tech. Report 50. U. S. 

Department of Agriculture, Forest Service; 154-161.

Objective 

To determine if P. picivorus posed any significant threat to regeneration in Phase II treatment stands.

Abstract

The pitcheating weevil, Pachylobius picivorus (Germar), is a major regeneration pest of pine stands, Pinus

spp., throughout the eastern USA. Adults feed nocturnally on the inner bark of seedlings and tree shoots, 

and oviposit in the roots and root collars of stressed or dying pine trees, as well as other coniferous hosts. 

As part of the Phase II Ecosystem Management Research conducted in the Ouachita and Ozark National 

Forests, the Arthropod and Microbial Communities Study Group completed a survey of P. picivorus.

Pitfall traps baited with 2 ml of turpentine (52.5% α-pinene, 41.4% β−pinene, 2% β-phellandrene and 

1.1% limonene) and 2 ml of 95% ethanol caught P. picivorus. Adequate sampling accuracy was obtained by 

establishing three transects of 10 traps each per site. Traps were spaced on 12 m intervals.

Sampling Procedure

Use one 2-ml vial of turpentine and one 2-ml vial of 95% ethanol in each PVC pitfall trap (for a 

description of the trap see Hunt and Raffa 1989). Remove all trap contents, clean and re-bait each trap 

weekly.

References

*Hunt, D. W. A.; Raffa, K. F. 1989. Attraction of Hylobius radicis and Pachylobius picivorus (Coleoptera: Curculionidae) 

to ethanol and turpentine in pitfall traps. Environmental Entomology 18: 351-355.
17

Pitcheating Weevil

Pachylobius picivorus (Germar) 

Coleoptera: Curculionidae

Rieske, L. K.; Raffa, K. F. 1993. Potential use of baited pitfall traps in monitoring 

pine root weevil, Hylobius pales, Pachylobius picivorus, and Hylobius radicis

(Coleoptera: Curculionidae) populations and infestation levels. Journal of 

Economic Entomology 86: 475-485.

Objective

To investigate the predictive potential of this monitoring system for use in an integrated pest 

management (IPM) system for P. picivorus.

Abstract

Plantation pine, Pinus spp., production in the Lake States is often limited by the feeding of adult 

pitcheating weevils, Pachylobius picivorus (Germar), which can cause extensive seedling mortality and 

disfigurement of young trees. Population fluctuations of P. picivorus were monitored from 1988 to 1990 in 

five, 5-year-old Scots pine, Pinus sylvestris (L.), plantations in central Wisconsin. Trees were spaced 1.8 m 

apart and had not received insecticide treatment. Pitfall traps were baited with 2 ml of turpentine (46% 

α-pinene, 42% β-pinene, 2% β-phellandrene, 1% limonene, 0.9% camphene and 0.8% myrcene) and 2 

ml of 95% ethanol and tested at 6, 18 and 32 traps per 432 m2

 to determine their ability to detect weevil 

activity. 

The lowest trap density (6 traps) had the best correlation between trap catch and damage indices, and 

was the least expensive. Trap catch of P. picivorus was correlated significantly with 1988 to 1990 foliar 

symptoms, suggesting that either assessing population levels of P. picivorus or their damage may be useful 

at forecasting future population trends and damage levels.

Sampling Procedure

Pitfall traps: Use 17 cm long by 10 cm wide plastic PVC pipes (see Tilles and others 1986). Drill eight 

7-mm wide entrance holes around the perimeter of each trap, 6 cm from the top end. Coat the inside 

of each trap with liquid Teflon (DuPont de Nemours, Wilmington, DE, USA) to prevent weevil escape. 

Drill two 2-mm holes in the trap wall just below the entrance holes. Attach a 14-gauge wire through these 

two holes. Place a 2-ml vial of 95% ethanol and a 2-ml vial of turpentine (Mantz Paint, Madison, WI) 

in each trap by suspending both vials from the wire approximately 4 cm below ground level. Cover each 

end of all traps with plastic caps. Drill two 2-mm holes in the bottom cap to drain water. Paint the above 

ground cap, and the exposed portion of the PVC pipe, black in order to resemble a tree trunk. Bury traps 

vertically until entrance holes are flush with ground level.

Space traps evenly within the 432 m2

 block (i.e., 8-9 m). Monitor all traps weekly throughout the activity 

period of P. picivorus. Remove all weevils and replenish all baits during each sampling interval. 

To determine infestation levels and tree damage in each 432 m2

 block, sub-sample an appropriate number 

of trees and look for the following:
18

Infestation levels: Examine the root collar of 12 trees per block for larval tunneling to a depth of 12 cm 

into the soil. Calculate the following two indices of infestation based on this data:

1. infestation level = # trees infested/# trees in subsample

2. infestation severity = (the sum of the proportion of damaged stem perimeters/# 

subsampled trees) x 100.

Incidence of foliar symptoms: Grade all trees in block based on needle color. Trees with green (visibly 

healthy), yellow (intermediate degradation) and red or brown (dead) needles should be given a 1, 2 and 3, 

respectively. Use this data to determine four indices of foliar symptoms:

1. symptom level = proportion of symptomatic trees 

2. foliar severity = sum of foliar grades/# trees in replicate 

3. proportion of yellow trees 

4. proportion of red trees

Note

Better damage estimates may be obtained if P. picivorus, pales weevil, Hylobius pales (Herbst.), and pine root 

collar weevil, Hylobius radicis Buchanan, are treated as a complex rather than trying to estimate damage 

separately for each of these closely related species.

Reference

Tilles, D. A., Sjödin, K.; Nordlander, G.; Eidmann, H. H. 1986. Synergism between ethanol and conifer host volatiles as attractants for the pine weevil, Hylobius abietis (L.) (Coleoptera: Curculionidae). Journal of Economic Entomology 79: 970-973.
19

White Grubs

Phyllophaga spp. 

Coleoptera: Scarabiidae

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

Entomologist 97: 596-604.

Objective

 To develop a sequential sampling plan for white grubs, Phyllophaga spp., which facilitates the estimation 

of population levels and helps determine if control measures are warranted.

Abstract

White grubs, Phyllophaga spp., feed on the roots of conifers in newly established plantations, causing 

crop failures in infested stands. A study was conducted in the Agassiz Forest Reserve in southeastern 

Manitoba, Canada to determine the vertical and frequency distribution of white grubs in a 929 cm3

 (1 ft3) 

sample area.

In Canada, white grubs complete a three-year life cycle with the majority of this time being spent in the 

larval (grub) stage. The best time to sample is during June and July when populations are estimated by 

counting the number of second and third instar larvae, pupae and adults. For the pre-planting survey, 

only first through third instar larvae are counted. Both surveys have an error of misclassification of 10%. 

Densities were classified as light (< 0.2), moderate (0.2-0.7) and severe (>1.1 per cubic foot). Control 

measures were advisable if a survey completed during the previous season estimated densities of greater 

than 0.7 larvae per cubic foot.

Sampling Procedure

Grub population density samples: Select a number of potential sample plots randomly throughout the 

area of concern. At each plot, remove all soil in a 30 by 30 cm surface area, digging into the soil to a 

depth of approximately 30 cm for a sample unit of 900 cm3

 (1ft3). Remove 10 cm of soil at a time and 

pass it through a screen to collect grubs. Include all white grubs found in the sample due to the difficulty 

of identifying grubs to species. Do not try to count eggs and first instars as they are very small and thus 

difficult to find in the soil. Reference Table II and continue sampling until a decision is met.

Pre-planting survey samples: Sample newly harvested areas in July of the year prior to planting. The 

number of first through third instar larvae must be counted to predict accurately the density of grubs the 

following season. First instar larvae at time of sampling will become second instar larvae the following 

spring, which are capable of causing considerable damage. Therefore, these samples take more time to 

process. It is recommended that two screenings be done for pre-planting samples. Use a larger screen to 

catch second and third instar larvae, and to remove coarse debris. Then re-screen with a finer mesh size 

to capture first instar larvae. Reference Table III and continue sampling until a decision is met.
20

Notes

Damage classifications are subjective because of insufficient data on the relationship between density and 

damage. The timing of samples will likely change according to latitude and adjustments should therefore 

be made for sampling populations in the USA.

Reference

Shenefelt, R. D.; Liebig, H. .R.; Dosen, R. C. 1955. Protecting machine transplanted trees from white grubs. Tree 

Planters’ Notes 20: 14-17. 

Tables 
21

Table II. Sequential sampling plan for classifying Phyllophaga spp. infestations. Sampling continues until 

the cumulative number of larvae (grubs) is ≤ or ≥ the tabulated values (refer to the original publications 

for sample sizes >40.)

Number ft3 soil 

samples examined

Cumulative number of white grubs

Light

CONTINUE

Moderate Moderate

CONTINUE

Severe

1 - - - 9

2 - - - 10

3 - - - 11

4 - - - 12

5 - - - 12

6 - - - 13

7 - - - 14

8 - - - 15

9 - - - 16

10 - - - 17

11 - - - 18

12 - - - 19

13 - - - 19

14 0 - - 20

15 0 - - 21

16 0 - - 22

17 1 - - 23

18 1 - - 24

19 1 - - 25

20 1 10 10 26

21 2 10 10 27

22 2 11 11 27

23 2 11 12 28

24 3 11 13 29

25 3 11 14 30

26 3 12 15 31

27 4 12 16 32

28 4 12 17 33

29 4 13 17 34

30 4 13 18 35

31 5 13 19 36

32 5 13 20 37

33 5 14 21 38

34 6 14 22 39

35 6 14 23 40

36 6 15 24 41

37 6 15 25 41
22

Tables reproduced with permission from the Canadian Entomologist, January 15, 2001.

38 7 15 25 41

39 7 15 26 42

40 7 16 27 43

Table III. Sequential sampling plan for use in pre-planting surveys to determine if white grub contol is 

needed. Sampling continues until the cumulative number of grubs is ≤ or ≥ the tabulated values.

30Number ft3 soil 

samples examined

Cumulative number of white grubs

No control needed

CONTINUE

Control needed

1 - 4

2 - 5

3 - 5

4 - 6

5 - 6

6 - 7

7 - 7

8 0 8

9 0 8

10 1 9

11 1 9

12 2 9

13 2 10

14 3 10

15 3 11

16 4 11

17 4 12

18 5 12

19 5 13

20 5 13

21 6 14

22 6 14

23 7 15

24 7 15

25 8 15

26 8 16

27 9 16

28 9 17

29 10 17

30 10 18
23

Conifer Swift Moth

Korscheltellus gracilis (Grote) 

Lepidoptera: Hepialidae 

Tobi, D. R.; Leonard, J. G.; Parker B. L.; Wallner, W. E. 1992. Survey methods, 

distribution, and seasonality of Korshceltellus gracilis (Lepidoptera: Hepialidae) 

in the Green Mountains, Vermont. Environmental Entomology 21: 447-452. 

Objective 

To develop methods for interpreting the basic biology and potential pest status of K. gracilis.

Abstract 

The larvae of the conifer swift moth, Korscheltellus gracilis (Grote), feed on the roots of red spruce, Picea 

rubens Sarg., balsam fir, Abies balsamea (L.), and the leaf petiole bases of mountain wood fern, Dryopteris 

campyloptera Clarkson. Although this particular outbreak was found in red spruce-balsam fir stands 

near Camels Hump Mountain, Vermont, K. gracilis larvae could possibly be present in other mountain 

areas having the same host species. Feeding by K. gracilis can impair the trees’ assimilation of water and 

nutrients, predispose roots to attack by root pathogens, reduce the regeneration potentials of red spruce 

and balsam fir, and cause decline or death.

This insect was found to have a two-year life cycle with greatest densities found above 885 m in elevation. 

Adult flight and mating occurred within a half hour before sunset and after sunrise, from late June 

through early August. Peak flight activity occurred during July, with the heaviest flights occurring on even 

numbered years. Greater numbers of K. gracilis were caught in Malaise traps than in 50-cm2

, clear plastic 

sticky traps placed 15 cm above the ground. However, Malaise traps were found to be too costly for 

widespread use.

Sampling Procedure

Interception trap study: Use sticky board traps to sample large stands and Malaise traps for smaller units.

Sticky traps: Two types of deployment can be used: circular and transect. For circular 

deployment, use 50 by 50-cm clear, 2.5 mm thick Plexiglas coated with Tangle Trap (Tanglefoot, 

Grand Rapids, MI, USA). Suspend traps from overhanging tree limbs with nylon cord such 

that they are approximately 15 cm above the forest floor. Space four to eight traps 6 m from a 

plot center for a spacing of 14-28 m along the circumference, respectively (plot spacing in this 

study was at least 60 m). For transect deployment, place one or two Plexiglas sticky traps at 30 m 

intervals within the area to be monitored. Regardless of the deployment, traps should be in place 

2-3 weeks before the initiation of flight activity (i.e., in this study early June), and maintained 

until 3 weeks after the end of flight activity (i.e., late August in this study). Check all traps daily 

during the flight period of K. gracilis.
24

Malaise traps: Place Malaise (BioQuip, Santa Monica, CA, USA) traps approximately 15 cm 

above the forest floor at the base of a potential host tree. Distribute traps on a 30-m spacing 

to obtain a representative sample of the area in question. Check all traps daily during the flight 

period of K. gracilis. 
25

Western Pine Shoot Borer

Eucosma sonomana Kearfott 

Lepidoptera: Tortricidae 

Sower, L. L.; Daterman, G. E.; Sartwell, C. 1984. Surveying populations of western pine 

shoot borers (Lepidoptera: Oleuthreutidae). Journal of Economic Entomology 

77: 715-719.

Objective

To compare different survey methods for estimating populations of E. sonomana in the field.

Abstract

The western pine shoot borer, Eucosma sonomana Kearfott, is a pest of ponderosa pine, Pinus ponderosa

Dougl. ex Laws., and can infest other pines in the western USA, by mining the pith of terminal shoots. 

This feeding causes an average 25% growth reduction in terminal shoots. A study was carried out in 

south-central Oregon to determine if the population level of E. sonomana could be accurately estimated 

using visual surveys, shoot dissections, and the trapping of male moths.

The visual assessment technique was determined to be the most practical for determining population 

levels of E. sonomana, especially if the investigators had experience assessing infestations. Visual surveys 

were also well correlated with the number of infested shoots as determined by shoot dissections as well 

as trap catches of male E. sonomana. Current-year infestation levels, as determined by the visual survey, 

accurately determined infestation levels the following year. Sampling at least 4 plots per plantation (10 

trees per plot) was recommended for trees less than 8 m tall. Also, to verify accuracy of the visual survey, 

a sub-sample of terminal and upper lateral shoots could be taken (10 shoots per tree) and dissected. 

Pheromone trapping for males was recommended if many plantations had to be surveyed. Pheromone 

traps should be placed in the mid-crown foliage of at least 5 sample trees at 20 m spacing.

Sampling Procedure

Visual survey: Pace 30 m directly into a plantation away from a road or plantation edge and select the 

nearest tree. Lay out a 50 by 50-m plot, marking a tree in each corner. Off each corner select a block of 

24 trees (3 rows of 8 trees), and visually inspect terminal and lateral shoots for presence of short needles, 

which is a characteristic symptom of E. sonomana infestation (Stoszek 1973). The authors suggest that 

the leaders on as few as 10 samples of 10 trees (100 shoots per plantation) could be surveyed to provide 

adequate sampling accuracy.

Shoot dissection survey: Although this method is by far the most accurate means of determining 

infestation levels, it requires the removal and destruction of the terminal shoot. Select three adjacent trees 

out from each marked corner of each plot for destructive sampling of the terminal shoot. Record shoots 

with larval mines greater than 5 cm in length as infested. This method is also complementary to the visual 

inspection of terminal leaders. For intensive, small-scale applications, sample destructively 10 vigorous 

lateral shoots on each of 10 trees per plantation to estimate infestation levels.

Pheromone traps: Use Pherocon II pheromone traps baited with a 0.005% pheromone in a 70 mg 

polyvinyl chloride (PVC) pellet (Daterman 1974). Place traps in the mid-crown of 5 trees on a 100-m 
26

loop (20 m spacing between traps) within the area to be treated. Set traps one week prior to the predicted 

flight period (i.e., April in south-central Oregon) and leave in place until a week after the predicted flight 

period (i.e., June). This trapping method is effective at detecting populations of E. sonomana over large 

scale areas. For intensive, small-scale applications, the use of many pheromone traps may actually reduce 

moth density when populations exist at low to moderate levels.

References

Daterman, G. E. 1974. Synthetic sex pheromone for detection survey of European pine shoot moth. Res. Pap. 

PNW-180. Corvallis, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station; 12 p.

Stoszek, K. J. 1973. Damage to ponderosa pine plantations by the western pine shoot borer. Journal of Forestry 71: 

701-705.
27

European Pine Shoot Moth

Rhyacionia buoliana (Denis & Schiffermüller) 

Lepidoptera: Tortricidae

Miller, W. E. 1960. The European pine shoot moth: relationship between proportion of 

trees infested and number of insects per tree. Journal of Forestry 58: 647-648. 

Objective 

To determine if the number of R. buoliana per tree was related positively to the proportion of trees 

infested by R. buoliana. 

Abstract

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

two- and three-needle pines, Pinus spp., in the northern USA following its introduction into Long Island, 

NY in 1914. This insect primarily infests terminal shoots causing severe deformation, and reduced 

growth. This study was conducted in red pine, Pinus resinosa Ait., plantations in Michigan.The number of 

R. buoliana per tree (Y) was related positively to the proportion of trees infested by R. buoliana (X) (Y = 

-1.09 + 0.02X). The sampling of at least 15 trees per stand to determine the proportion of infested trees 

was recommended.

Sampling Procedure 

Use this method in pine plantations at 1.8 by 1.8-m spacing and from approximately 1 m in height to 

crown closure. Sample at least 15 trees in the area of concern by selecting every fifth tree in every fifth 

row. Inspect the terminal bud cluster of each sample tree for pitch globules and distorted or dying shoots 

(i.e., shepherd’s crooking). Damage from R. buoliana persists throughout the winter so this method can 

be carried out when populations are in diapause. Enter the proportion of infested trees (X) into the 

equation Y = -1.09 + 0.02X to determine the number of R. buoliana per tree (Y).

During April and June in lower Michigan, the late larval and pupal stages could be collected and sent for 

identification to confirm the presence of R. buoliana to distinguish from other closely related species in 

the genus Rhyacionia. 

Note

The inter-tree distribution of R. buoliana was random within all plantations.
28

Nantucket Pine Tip Moth

Rhyacionia frustrana (Comstock) 

Lepidoptera: Tortricidae

Fettig, C. J.; Berisford, C. W. 1999. A comparison of three common indices for estimating 

Nantucket pine tip moth damage in the field. Journal of Entomological Science 

34: 203-209.

Objectives

To determine if terminal damage was correlated positively to top whorl and whole tree damage; and if 

top whorl damage was correlated positively to whole tree damage.

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. Four loblolly pine plantations 

were sampled in the Georgia Piedmont and four in the Georgia and South Carolina Coastal Plain. 

Sampling was conducted three times annually in the Piedmont and four times annually in the Coastal 

Plain, and coincided with the pupal stage of each generation.

Terminal damage (Y) was correlated positively with top whorl (Y = 0.04 + 1.29X, R = 0.87, P < 0.0001), 

and with whole tree damage (Y = 0.17 + 1.23X, R = 0.71, P < 0.0001). Top whorl damage (Y) was 

correlated positively with whole tree damage (Y = 0.09 + 0.99X, R = 0.86, P < 0.0001). Top whorl 

damage indices were the most sensitive indicator of damage levels in all tree strata examined.

Sampling Procedure 

Select sample trees randomly, and count the number of damaged and undamaged shoots in the terminal 

and first cluster of lateral shoots (usually 3-6) beneath the terminal (e.g., top whorl). A shoot is defined as 

greater than 10 cm of apical stem containing foliage. The terminal is the new leader occurring at the top 

of the main stem. If the leader is damaged severely or aborted by causes other than R. frustrana, consider 

the tallest lateral shoot of the top whorl growing in a vertical orientation to be the terminal. Calculate 

the proportion of infested shoots, (# infested/total number) x 100, in the top whorl. Top whorl damage 

indices may be the best compromise for estimating R. frustrana damage when considering the time 

constraints of obtaining more detailed estimates versus allocating that time toward another sample.

Note

The associations between top whorl damage and terminal and whole-tree damage may be limited to trees 

less than three years old.
29

Nantucket Pine Tip Moth

Rhyacionia frustrana (Comstock) 

Lepidoptera: Tortricidae 

Gargiullo, P. M.; Berisford, C. W. 1981. Sampling for pine tip moths-a procedural guide. 

Res. Bull. 272. Athens: The University of Georgia; 25 p. 

Objective 

To determine the mean number of immature R. frustrana per shoot, per tree, and per unit area for 

developing life tables and absolute population estimates.

Abstract 

The Nantucket pine tip moth, Rhyacionia frustrana (Comstock), is an important pest of Christmas tree and 

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

and volume reductions, formation of compression wood, and occasional tree mortality. Damage is most 

severe on seedlings and saplings less than 5-years-old. This paper discusses field sampling procedures and 

the use of a FORTRAN program to provide estimates of the mean number of immatures per shoot, per 

tree, and per area (e.g., per ha), with respective variances.

Sampling Procedure 

The sampling procedure is described clearly in Gargiullo and others (1983). Once the data is collected, a 

FORTRAN program can be used to compute estimates of population size. Refer to Appendix B for the 

FORTRAN coding.

Note

Do not confuse the larvae of R. frustrana with those of R. rigidana. The relative position of the three 

prespiracular setae are used to differentiate these two insects (Miller and Wilson 1964).

References

* Gargiullo, P. M.; Berisford, C. W.; Pienarr, L. V. 1983. Two-stage cluster sampling for pine tip moths. Environmental Entomology 12: 81-90.

Miller, W. E.; Wilson, L. F. 1964. Composition and diagnosis of pine tip moth infestations in the southeast. Journal 

of Economic Entomology 57: 722-726.
30

Nantucket Pine Tip Moth

Rhyacionia frustrana (Comstock) 

Lepidoptera: Tortricidae

Gargiullo, P. M.; Berisford, C. W.; Pienaar, L. V. 1983. Two-stage cluster sampling for 

pine tip moths. Environmental Entomology 12: 81-90. 

Objective 

To develop a sampling scheme for R. frustrana for a known SE.

Abstract

The Nantucket pine tip moth, Rhyacionia frustrana (Comstock), is a serious pine regeneration pest. 

Larvae feed within newly developing shoots causing flagging, tree deformity and reductions in growth. 

In the Georgia Piedmont, there are three generations per year with the pupa of the third generation 

overwintering inside the shoots. A sampling scheme was described for the immature stages of R. frustrana, 

on 3-year-old loblolly pine, Pinus taeda L., in the Georgia Piedmont.

Sampling involved a two-stage process whereby trees were selected randomly (stage 1) and then the 

crown stratified into two levels, with shoots being sampled in each level (stage 2). Depending on the 

desired standard error (SE), from 2 (50% SE) to 454 (5% SE) trees are sampled. On these trees, as few 

as 2 or as many as 5 shoots were sampled per level. The time required to complete a sample was inversely 

related to desired SE. 

Sampling Procedure 

Select the desired SE (Table 1). Sample the number of trees indicated in Table 1 randomly throughout 

the area of concern. On all sample trees less than 0.5 m tall, treat the entire live crown as level 1 and 

sample according to desired SE (i.e., if SE was 10%, then 2 shoots would be sampled) (Table 1). On 

trees greater than 0.5 m tall, divide the live crown into two strata. Sample each level according to the 

desired SE. All samples, regardless of location within the crown, should be chosen to include nearly equal 

amounts of foliage. Thus, shoots should be non-overlapping and account for all foliage within a level. 

Shoots are numbered, clipped, placed into bags, and then put in a cooler.

In the lab, examine shoots for the life stages of R. frustrana. Counts are recorded according to shoot, level, 

and tree. A FORTRAN program along with documentation has been written to compute the necessary 

statistics presented in this article as well as procedures used to handle trees up to 2.5 m tall (Gargiullo and 

Berisford 1981).

Note

Do not confuse the larvae of R. rigidana with those of R. frustrana. The relative positions of the three 

prespiracular setae are used to distinguish between the two species (Miller and Wilson 1964).
31

References 

*Gargiullo, P. M.; Berisford, C. W. 1981. Sampling for pine tip moths-a procedural guide. Res. Bull. 272. Athens: The 

University of Georgia. 25 p.

Miller, W. E.; Wilson, L. F. 1964. Composition and diagnosis of pine tip moth infestations in the southeast. Journal 

of Economic Entomology 57: 722-726. 

Table 

Desired SE (%)b No. of trees No. of shoots 

in stratum 1

No. of shoots 

in stratum 2

Total cost of sample 

(human-h)

5 454 2 2 781.0

10 114 2 2 197.0

15 51 2 2 88.5

20 29 2 2 50.5

25 18 2 2 31.5

30 8 3 4 22.4

35 9 2 2 15.9

40 6 2 3 12.4

45 3 3 5 9.4

50 2 4 5 7.2

Computed for the overall density of R. frustrana immature stages, based on pilot sample number 19097. 

b SE (%) = (variance of the unbiased mean per tree estimate)1⁄2÷ unbiased mean per tree estimate.

Table 1 reprinted with permission from Environmental Entomology, January 15, 2001.
32

Nantucket Pine Tip Moth

Rhyacionia frustrana (Comstock) 

Lepidoptera: Tortricidae

Waters, W. E. 1974. Sequential sampling applied to forest insect surveys. In: Proceedings 

of IUFRO/SAF/SUNY symposium on monitoring forest environments through 

successive sampling. June 24-26; Syracuse, NY; 290-311. 

Objective

To develop a sequential sampling plan for the integrated pest management of R. frustrana. 

Abstract

A sequential sampling plan was developed from data on the Nantucket pine tip moth, Rhyacionia 

frustrana (Comstock), infesting loblolly pine, Pinus taeda L., in eastern Maryland. Larval feeding can cause 

substantial terminal and subterminal shoot deformation and reduced growth. In Maryland, R. frustrana

has 2-3 generations a year with pupae of the final generation overwintering inside shoots. This sampling 

plan was intended for the first generation of R. frustrana as a basis for deciding if control measures were 

needed for the next generation.

A maximum of 50 trees, selected randomly, was recommended at each survey point. If <10% of trees 

had infested terminals, then control was not recommended. If >20% of trees had infested terminals, 

then control was recommended. If the percentage of infested terminals was between 10-20% after all 50 

trees had been sampled, then control was warranted with an estimate >15%, and not warranted with an 

estimate <15%. 

Sampling Procedure

Select sample trees randomly in the area of concern. Examine the terminal bud cluster for the presence 

of R. frustrana. If R. frustrana is present, then record the terminal as infested. Reference the sequential 

sampling plan, and continue sampling until a decision is met (Fig. 1). A maximum of 50 trees is sampled 

at each survey point. If after sampling 50 trees the cumulative count of infested trees lies in the continue 

sampling zone (i.e., no decision zone), then select the category closest to this count. Changing either the 

class limits or the risk levels will alter the size of the no decision zone as well as changing the number of 

terminals to be examined (Figs. 2, 4).

Note

The distribution of R. frustrana follows a binomial distribution. 
33

Figures

Figure 1. Sequential graph for sampling Nantucket pine tip moth in loblolly pine.
34

Fig. 2. Comparison of decision boundaries of sequential sampling plan for Nantucket pine tip moth in 

loblolly pine: A with gap between m1 and m2 reduced, and B with risk levels reduced.
35

Figures reprinted with permission from the IUFRO/SUNY symposium, January 15, 2001.

Fig. 4. Average Sample Number curve(s) for the Nantucket pine tip moth sequential sampling plan(s).
36

Pitch Pine Tip Moth

Rhyacionia rigidana (Fernald) 

Lepidoptera: Tortricidae 

Gargiullo, P. M.; Berisford, C. W. 1981. Sampling for pine tip moths-a procedural guide. 

Res. Bull. 272. Athens: The University of Georgia; 25 p. 

Objective 

To determine the density of immature R. rigidana in order to develop life tables and absolute population 

estimates.

Abstract

The pitch pine tip moth, Rhyacionia rigidana (Fernald), is a common associate of the more abundant 

Nantucket pine tip moth, Rhyacionia frustrana (Comstock), and often shares the same host. Both species 

are important regeneration pests of pine plantations in the eastern USA. This paper discusses field 

sampling procedures and the use of a FORTRAN program to provide estimates of the mean number of 

immatures per shoot, tree, and unit area (e.g., per ha) with known levels of precision.

Sampling Procedure 

The basic sampling procedure is described clearly in our review of Gargiullo and others 1983. Once 

the data has been collected appropriately, a FORTRAN program can be used to compute estimates of 

population size. Refer to Appendix B in the original publication for the FORTRAN coding.

Note

Do not confuse the larvae of R. rigidana with those of R. frustrana. The relative positions of the three 

prespiracular setae are used to distinguish between the two species (Miller and Wilson 1964).

References

*Gargiullo, P. M.; Berisford, C. W.; Pienarr, L. V. 1983. Two-stage cluster sampling for pine tip moths. Environmental Entomology 12:81-90.

Miller, W. E.; Wilson, L. F. 1964. Composition and diagnosis of pine tip moth infestations in the southeast. Journal 

of Economic Entomology 57:722-726.
37

Pitch Pine Tip Moth

Rhyacionia rigidana (Fernald) 

Lepidoptera: Tortricidae 

Gargiullo, P. M.; Berisford, C. W.; Pienaar, L. V. 1983. Two-stage cluster sampling for 

pine tip moths. Environmental Entomology 12: 81-90. 

Objective 

To develop a sampling scheme for R. rigidana for a known SE.

Abstract

The pitch pine tip moth, Rhyacionia rigidana (Fernald), is a common associate of the more abundant 

Nantucket pine tip moth, Rhyacionia frustrana (Comstock), and often shares the same host. Larvae feed 

within newly developing shoots causing flagging, tree deformity and reductions in growth. In the Georgia 

Piedmont, there are two generations per year with the pupa of the second generation overwintering 

inside the shoots.

A sampling scheme was described for the immature stages of R. rigidana, on 3-year-old loblolly pine, Pinus 

taeda L., in the Georgia Piedmont. Sampling involved a two-stage process whereby trees were selected 

randomly (stage 1) and then the crown stratified into two levels, with shoots being sampled in each level 

(stage 2). Depending on the desired standard error (SE), from 2 (50% SE) to 454 (5% SE) trees are 

sampled. On these trees, as few as 2 or as many as 5 shoots were sampled per level. The time required to 

complete a sample was related negatively to desired SE.

Sampling Procedure

Select the desired SE (Table 1; see p. 31). Sample the number of trees indicated in Table 1 randomly 

throughout the area of concern. On all sample trees less than 0.5 m tall, treat the entire live crown as 

level 1 and sample according to desired SE (i.e., if SE was 10%, then 2 shoots would be sampled) (Table 

1). On trees greater than 0.5 m tall, divide the live crown into two levels. Sample each level according to 

the desired SE. All samples, regardless of location within the crown, should be chosen to include nearly 

equal amounts of foliage. Thus shoots should be non-overlapping and account for all foliage within a 

level. Shoots are numbered, clipped, placed into bags and then put in a cooler. In the lab, examine shoots 

for the life stages of R. rigidana. Counts are recorded according to shoot, level, and tree. A FORTRAN 

program along with documentation has been written to compute the necessary statistics presented in this 

article as well as procedures used to handle trees up to 2.5 m tall (Gargiullo and Berisford 1981).

Note

Do not confuse larvae of R. rigidana with those of R. frustrana. The relative positions of the three 

prespiracular setae used to distinguish between the two species (Miller and Wilson 1964). 
38

References

*Gargiullo, P. M.; Berisford, C. W. 1981. Sampling for pine tip moths-a procedural guide. Res. Bull. 272. Athens: The 

University of Georgia. 25 p.

Miller, W. E.; Wilson, L. F. 1964. Composition and diagnosis of pine tip moth infestations in the southeast. Journal 

of Economic Entomology 57: 722-726.
39

Spruce Budmoth

Zeiraphera canadensis Mutuura and Freeman 

Lepidoptera: Tortricidae

Turgeon, J. J.; Régnière, J. 1987. Development of sampling techniques for the spruce 

budmoth, Zeiraphera canadensis Mut. and Free. (Lepidoptera:Tortricidae). 

Canadian Entomologist 119: 239-249.

Objectives

To develop a practical and meaningful sample unit for assessing Z. canadensis populations; to determine 

the required sample size for density estimates with a given level of precision; and to develop a sequential 

sampling plan for Z. canadensis.

Abstract

The spruce budmoth, Zeiraphera canadensis Mut. and Free., is a serious pest of young white spruce, Picea 

glauca (Moench) Voss, plantations. Larvae feed on newly developing terminal shoots causing multiple 

leaders, crown deformation, and growth loss.

A 15-cm branch segment, measured distally from the scales of the branch’s apical growth and taken from 

the upper one-third of the crown, was considered an adequate sample unit for density estimates of Z. 

canadensis eggs and larvae. A minimum of 5 and maximum of 100 branch samples was recommended 

(Fig. 7). Populations of Z. canadensis were considered high and thus potentially damaging if more than 

5 larvae were found per upper crown branch segment. Control measures were deemed necessary for 

populations exceeding this threshold level. Populations were considered low, and thus not potentially 

damaging, if less than 5 larvae were found per upper crown branch segment. The authors stated that to 

obtain accurate estimates of Z. canadensis population levels one need only sample trees less than 4 m in 

height.

Sampling Procedure

Select at least 5 trees randomly within the area of concern. Cut a 15-cm branch segment from the upper 

third of the crown of each tree (Fig. 1). Sample until the cumulative number of larvae rises above or 

drops below the decision thresholds (Fig. 7).

Z. canadensis eggs: If the population of Z. canadensis is still in the egg stage, then cut out the previous 

years’ bud scales (Fig. 1) and place them in a Mason jar. Place a lid on the Mason jar, replacing the metal 

center with an equal size piece of filter paper. Store at room temperature with at least 16 h of light daily. 

Each day, check all jars, count and remove all Z. canadensis larvae. Maintain colony a week after the last 

larva has been removed (note: larvae may need to be reared on budmoth diet until they can be correctly 

identified as Z. canadensis). This method can be carried out well before Z. canadensis becomes active in the 

field, providing enough preparation time if control measures are determined to be necessary.

Z. canadensis larvae: If the population of Z. canadensis is in the larval stage, and the majority of shoots have 
40

not elongated and still have bud scales on their tips, then sample as described at the beginning of this 

section. Each shoot will need to be dissected by removal of the bud scale covering the tip of each shoot. 

Feeding Z. canadensis or their damage should be readily visible beneath the bud scale. If the majority of 

shoots have elongated and most of the bud scales have fallen from the tips of the shoots, then sample 

as described at the beginning of this section. Shoots with bud scales still attached to the shoot tip will 

usually contain a larva. However, examine carefully all shoots for presence of this forest pest.

Figures

Fig. 1. Illustration of a white spruce branch (late July), showing the terms defined and the 

measurement methods described in the text.
41

Figures 1 and 7 reprinted with permission from the Canadian Entomologist, January 15, 2001.

Fig. 7. Sequential sampling plan for larvae of Z. canadensis in the upper third of crown of white 

spruce based on a critical level of five larvae per 15-cm branch segment. Minimum sample size: 5; 

maximum: 100; equation [7]).

DEFOLIATING INSECTS

43

Elm Leaf Beetle

Pyrrhalta luteola (Müller) 

Coleoptera: Chrysomelidae

Dahlsten, D. L.; Rowney, D. L.; Lawson, A. B. 1998. IPM helps control elm leaf beetle. 

California Agriculture 52: 17-23.

Objective

To develop a monitoring system for P. luteola for control decision-making.

Abstract

The elm leaf beetle, Pyrrhalta luteola (Müller), is one of the most important urban elm, Ulmus spp., pests 

in the USA and Canada. Most damage is caused by larval feeding, which skeletonizes the leaves. Recently, 

monitoring methods have become an integral part of the Integrated Pest Management (IPM) program 

for P. luteola. A monitoring program was developed from 1984-1993 in California, which predicts damage 

levels based on the occurrence of egg clusters on eight elm branch terminals in the lower crown of each 

tree sampled. 

Sampling Procedure

Refer to Table 1 to determine the number of trees to be sampled depending on surrounding elm 

densities. Sample trees should be selected randomly for the first sample, and then resampled again on 

each sample date. Collect one 30-cm branch tip from both the inner and outer crown in four cardinal 

directions (n = 8). Remove samples during peak egg deposition (233 and 903 degree-days °C) for 

generations 1 and 2.

Record damage ratings by visual estimation of adult and larval defoliation on each branch terminal 

sampled from a scale of 0-10, where 0 equals 0% defoliation and 10 equals 100% defoliation. In locations 

having one generation per season, damage is assessed at the end of the season in the fall. For locations 

having greater than one generation per season, damage is rated for each generation. A damage threshold 

of 4 (40% defoliation) was established for the first generation. If 45% or less of samples have viable egg 

clusters present, then damage at the end of that generation will be tolerable and control is not warranted 

(probability of error = 10%). In the second generation, a maximum of 30% defoliation is acceptable. 

Weekly monitoring, beginning at approximately 50 degree-days (°C) before the predicted peak egg 

deposition for each generation and continuing for an additional week, was recommended.

Notes

Degree-day predictions for peak egg deposition are based on a lower developmental threshold of 11°C 

with accumulations beginning March 1. The corresponding predictions for each generation may vary with 

location. This method can also be used to estimate egg parasitism rates for evaluating the effectiveness of 

a biocontrol program.
44

Table reprinted from Dahlsten and others 1998, California Agriculture 52: 17-23, copyrighted by 

University of California Regents.

Table

Table 1. Suggested sample size for elm leaf beetle egg cluster monitoring on English elm in stands of 

different sizes. Eight locations per tree are sampled: north, east, south and west; inner and outer crown.

Total trees Sample trees Samples/tree Samples/segment Total samples Trees (%)

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

Criteria: (1) Minimum of 128 branches should be sampled. (2) Minimum of 25% of the trees should be sampled.
45

Redheaded Pine Sawfly

Neodiprion lecontei (Fitch) 

Hymenoptera: Diprionidae

Wilson, L. F.; Wilkinson, R. C., Jr.; Averill, R. C. 1992. Redheaded pine sawfly—its 

ecology and management. Agric. Handbook 694. East Lakewood, CO: U.S. 

Department of Agriculture, Forest Service, Rocky Mountain Region; 54 p.

Objectives

To evaluate the risk of potential injury from N. lecontei; to detect if N. lecontei or its damage is present at 

any particular time or place; and to evaluate population density or its potential to cause injury.

Abstract

The redheaded pine sawfly, Neodiprion lecontei (Fitch), is a major regeneration pest of pines, Pinus spp., in 

the eastern USA. The larvae feed gregariously on new and old needles of most eastern pines and also on 

the tender bark of young seedlings. The degree of damage is highly variable, depending on stress levels 

of infested trees. Young pines in plantations and nurseries are particularly susceptible to damage, and 

therefore need to be monitored regularly.

Several kinds of surveys are available for rating the risk of potential damage from N. lecontei and for 

detecting, evaluating, and suppressing populations. Risk of injury is a concern even before trees are 

planted because the condition of the site can affect the fecundity and survival of N. lecontei. Should the 

risk of injury be detected from the survey, management guidelines can be applied that maintain healthy, 

productive plantations.

Sampling Procedure

Risk survey: Prospective pine sites should be rated for risk of potential N. lecontei injury before planting. 

Risk ratings on established sites are not necessary if pines are taller than 4.5 m in the North, and taller 

than 8 m in the South. To evaluate risk, consider proximity to brood sources (do not plant within 1.6 

km), soil type and moisture holding capacity, associated vegetation, and previous land use. Please refer to 

original publication for more details.

Detection survey: Detection surveys are usually evaluated on the ground either casually or systematically, 

but can also be done via low flying aircraft when infestation levels are heavy. Once presence of the 

insect is detected by any one method, cease surveying. Sample high risk areas until one of the following 

symptoms or signs is found

Damage: Look for clusters of needles that have been skeletonized and resemble bottle brushes. 

This type of injury indicates that feeding has begun recently. In most instances, new and old 

needles will be missing on portions of the branches.

Eggs: Eggs are laid in the needles and appear as a row of cream yellow spots on the edge of 
46

needles growing in a cluster (Fig 1). In the North, eggs are deposited on the old growth needles 

only. In the South, eggs may also be deposited on newly formed needles. Egg-bearing new 

growth needles can often be identified at a distance because they curl in response to infestation.

Larvae: Larvae are observed easily as they tend to feed gregariously. Colonies of N. lecontei larvae 

from a few up to 100 individuals will be found on the edges of defoliated branches feeding on 

the remaining green foliage (Fig. 2).

Frass: Small piles of frass may be noticeable on the ground below the defoliated branches. Each 

pellet is cylindrical in shape with oblique ends, and is less than 2 mm in length and 1 mm wide.

Cocoons: To find cocoons, search the upper 5 cm of soil beneath the crowns of defoliated trees.

Adults: Male N. lecontei can be monitored by using pheromone-baited sticky traps. Place the trap 

at 1-2 m intervals within trees before the predicted male flight period and monitor regularly.

Evaluation survey: In order to obtain accurate information, timing is a critical component to evaluation 

surveys. Since N. lecontei phenology follows closely that of its host, the use of indicator plants along with 

particular events in their life cycle can aid in predicting emergence.

Surveys for eggs and larvae: Samples should be taken systematically along transects every 25 m. 

Examine each tree carefully for the presence of eggs and larvae, and record each tree as either 

infested or uninfested (1 = infested and 0 = uninfested). Sampling should continue until all the 

high risk areas are surveyed. When sampling is complete, calculate the percentage of trees that 

are infested. Depending upon location and tree size, different decision levels apply:

Location Tree Height

If the infestation 

level is . . . Then . . . 

North

< 2m ≤ 10%

CONTROL

IS

JUSTIFIED

> 2m ≥ 20%

South

< 3m ≤ 10%

> 3m ≥ 20%

Damage surveys: This survey helps to determine tree mortality and growth loss of northern 

pines. Unlike most southern pines, northern pine species usually succumb to defoliation levels 

greater than 90% in a single year (Benjamin 1955). Conduct the survey systematically, recording 

the nearest tree every 10 paces as either <90% defoliated, or >90% defoliated. At the end of the 

survey, determine the proportion of trees having >90% defoliation, and consider this figure to 

represent the expected mortality rate. Trees suffering <90% defoliation eventually recover well 

enough to avoid significant growth losses at harvest.

Note

The reader must have a significant understanding of the phenology of N. lecontei in their region in order 

to time the sampling of specific life stages properly in the field.
47

Reference

Benjamin, D. M. 1955. The biology and ecology of the red-headed pine sawfly. Tech. Bull. 1118. Washington, 

DC: U.S Department of Agriculture, Forest Service; 57 p. 

Figures:

Fig. 1 - Eggs appear as rows of yellow-white spots 

along the edges of individual needles growing in a 

cluster. UGA 0284086 www.forestryimages.org
48

Fig. 2 - Colonies of up to 100 larvae are usually found 

on the needles at the juncture of the foliage and the 

defoliated branch. 

UGA 5383259 www.forestryimages.org
49

Red Pine Sawfly

Neodiprion nanulus nanulus Schedl. 

Hymenoptera: Diprionidae

Connola, D. P.; Waters, W. E.; Nason, E. R. 1959. A sequential sampling plan for redpine sawfly Neodiprion nanulus Schedl. Journal of Economic Entomology 52: 

600-602.

Objective

To develop a sequential sampling plan for N. nanulus nanulus defoliation.

Abstract

The red pine sawfly, Neodiprion nanulus nanulus Schedl., can cause considerable damage to red pine, Pinus 

resinosa Ait., plantations in the northeastern USA and Canada. The larvae are gregarious and usually 

consume all of the mature needles from one branch before migrating to another branch. Little or no 

tree mortality results from complete defoliation in a single year, but can occur with repeated defoliation 

in multiple years. Eggs are laid in the needles in early fall, and do not hatch until the following spring, 

which offers ample opportunity to determine if control is warranted. A sequential sampling plan, based 

on the number of egg-infested needles on a 15-cm twig, is presented to predict damage levels. Damage is 

classified as either zero to light or moderate to heavy defoliation.

One twig is sampled from each of five trees, the cumulative number of egg infested needles is recorded, 

and then compared to the sequential sampling table (Table 1). If further sampling is required to reach a 

decision, the sixth sample should be taken from the first tree sampled. This method continues until each 

of the five trees have been sampled up to five times in order to reach a decision. If after 25 twig samples 

are obtained no classification decision is reached, then decisions are made on the basis of whether or not 

the count is closer to 21 (zero to light infestation) or 49 (moderate to heavy infestation) and classified 

accordingly. A field validation test showed that predictions failed only once in 25 attempts (i.e., 96% 

precision).

Sampling Procedure

Follow the sequential plan in Table 1. Sampling sites should be distributed evenly within a plantation. 

In large plantations, sampling should be done every 1.6-2 ha. Sample in fall and winter when eggs are 

present.

Sample one 15-cm long twig from each of five trees. Count and record the cumulative number of egg 

infested needles and then refer to Table 1. If further sampling is necessary to reach a decision, then take 

a sixth sample from the first tree sampled. This method continues until each of the five trees have been 

sampled up to five times. If after 25 twig samples are obtained no decision is reached, then base your 

decision on whether or not the count is closer to 21 (zero to light infestation) or 49 (moderate to heavy 

infestation). The confidence level for this plan was set at 90%.
50

Table

Table 1 reprinted with permission of the Journal of Economic Entomology, January 15, 2001.

Table 1. Sequential sampling plan for red-pine sawfly egg populations on 15 cm red pine tips.

No. twig samples

Cumulative total number of egg-infested needles

No. expected to produce

zero to light defolation

Range within which the 

amount of defoliation

expected is doubtfula

No. expected to produce

moderate to heavy

defoliation

1 — 0-14 ≥15

2 — 0-16 ≥17

3 — 0-17 ≥18

4 — 0-19 ≥20

5 — 0-20 ≥21

6 — 0-21 ≥22

7 — 0-23 ≥24

8 — 0-24 ≥25

9 — 0-26 ≥27

10 0 1-27 ≥28

11 ≤1 2-28 ≥29

12 ≤3 4-30 ≥31

13 ≤4 5-31 ≥32

14 ≤5 6-33 ≥34

15 ≤7 8-34 ≥35

16 ≤8 9-35 ≥36

17 ≤9 10-37 ≥38

18 ≤11 12-38 ≥39

19 ≤12 13-40 ≥41

20 ≤14 15-41 ≥42

21 ≤15 16-42 ≥43

22 ≤17 18-44 ≥45

23 ≤18 19-45 ≥46

24 ≤19 20-47 ≥48

25 ≤21 22-48 ≥49

*Continue sampling if count falls in this column. Note: Chances are 1 in 10 that defoliation will be predited 

incorrectly.
51

European Pine Sawfly

Neodiprion sertifer (Geoffroy) 

Hymenoptera: Diprionidae

Talerico, R. L.; Wilson, R. W., Jr. 1978. A sampling device for counting insect egg 

clusters and measuring vertical distribution of vegetation. Research Note NE250. Broomall, PA: U.S. Department of Agriculture, Forest Service, Northeastern 

Forest Experiment Station; 4 p.

Objective

To develop an efficient, accurate sampling method to estimate N. sertifer density in the field.

Abstract

The 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. The pest feeds on two- and 

three-needle pines, but often causes little damage as feeding occurs almost exclusively on old foliage. 

Eggs are laid in the fall in loose clusters, and are often used as a means to estimate population density. 

Lyons (1964) recommended the use of whole trees as sample units, which are rather costly to sample 

unless the trees are quite small. The use of a vertical sampling pole that delineates known foliage volumes 

was used to count N. sertifer eggs and egg clusters.

The sampling pole was a 2.5 cm diameter and 183 cm long hardwood pole intersected with wooden 

dowels at 30 cm intervals. This tool can be used to quantify the amount of foliage, estimate coverage, and 

to determine the distribution of damage. The number of eggs per cluster was found to range from 60-

170, with a mean of 59.3 ± 6.28 eggs per cluster, using this method.

Sampling Procedure

The authors describe the sampling pole as a hardwood pole, 2.54 cm in diameter and 183 cm in length, 

intersected by 0.95 cm diameter wooden dowels at 30 cm intervals. Each dowel is at a right angle to 

the one below. Drill a 0.5 cm vertical hole near the end of each dowel (i.e., holes are 21 cm apart), and 

string a sighting wire from top to bottom of the pole through each hole (refer to Fig. 2 in the original 

publication). The dowels and sighting wires delineate a 7000 cm3

 volume.

Place the sampling pole as close as possible to the sample tree. Record the amount of current year’s 

foliage, and number of eggs and egg clusters within each vertical sample unit. Divide the total number of 

eggs or egg clusters by the amount of current year’s foliage to determine egg density per unit foliage.

Note

Variable sampling volumes can be created by simply changing the distance between dowels and sighting 

wires.

Reference

Lyons, L. A. 1964. The spatial distribution of two pine sawflies and methods of sampling for the study of 

population dynamics. Canadian Entomologist 96: 1373-1407.
52

European Pine Sawfly

Neodiprion sertifer (Geoffroy) 

Hymenoptera: Diprionidae

Wilson, L. F.; Gerrard, G. J. 1971. A new procedure for rapidly estimating European pine 

sawfly (Hymenoptera: Diprionidae) population levels in young pine plantations. 

Canadian Entomologist 103: 1315-1322.

Objective

To estimate the population density of N. sertifer based on the proportion of infested trees in a random 

sample.

Abstract

The 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. The pest feeds on two- and 

three-needle pines, but often causes little damage as feeding occurs almost exclusively on old foliage. A 

method was proposed for rapidly estimating the population levels of N. sertifer in young red, Pinus resinosa

Aiton, and Scots pine, P. sylvestris L., plantations. 

Sampling Procedure

An estimate of the mean number of N. sertifer larvae per tree (Y) may be predicted from the proportion 

(p) of trees infested by the equation

Y = k [(1/1-p)

1/k - 1]

where k is an estimate of a distribution parameter, and is derived beforehand by Maximum Likelihood 

from a series of insect populations representative of those where predictions are contemplated. Data 

from sampled sites derived a k-value of 1.37, yielding a curve that accounted for 91% of the variation 

among the means. To prevent loss of growth and vigor, control should be considered on trees 1.5-2.5 m 

tall when the mean number of colonies (Y) is ≥5 colonies per tree (Wilson 1966).

Notes

While the population means may vary from plantation to plantation, the aggregation index (k) is 

constant. The proportion of samples greater than 0.9 does not make adequate estimates and needs 

additional points for improvement. Due to the detailed nature of this particular study, we refer you to the 

origin publication for more information concerning the sampling procedures and associated statistical 

equations.

Reference

Wilson, L. F. 1966. Effects of different population levels of the European pine sawfly on young Scotch pine 

trees. Journal of Economic Entomology 59: 1043-1049.
53

Swaine Jack Pine Sawfly

Neodiprion swainei Middleton 

Hymenoptera: Diprionidae

Tostowaryk, W.; McLeod, J. M. 1972. Sequential sampling for egg clusters of the Swaine 

jack pine sawfly, Neodiprion swainei (Hymenoptera: Diprionidae). Canadian 

Entomologist 104: 1343-1347.

Objective

To develop a sequential sampling plan that determines the intensity of N. swainei infestations.

Abstract

The Swaine jack pine sawfly, Neodiprion swainei Middleton, is one of the most important defoliators of 

pine, Pinus spp., throughout Canada and the Lake States. The larvae are gregarious and feed primarily 

on older foliage. Numerous outbreaks occur on regular eight year cycles in jack pine, P. banksiana

Lamb., stands in Ontario and Quebec. Heavy tree mortality often occurs in stressed, senescing stands. A 

sequential sampling method for egg clusters of N. swainei is described that classifies infestations as either 

light (≤ 3.3 egg clusters per tree), moderate (8.3-14 egg cluster per tree) or severe (≥26 egg clusters per 

tree). The maximum number of trees to be sampled is 10. 

Sampling Procedures

Select a codominant or dominant jack pine randomly within the area of concern and fell the tree. 

Remove, count, and record the number of shoots bearing egg clusters. Sampling follows the sequential 

table until the cumulative number of egg clusters exceeds a decision level (Table 2). The maximum 

number of trees to be sampled is 10. If after sampling 10 trees no decision is met, the following rule is 

applied

Light-moderate band: if d <51 classify as light or ≥51 classify as moderate

Moderate-severe band: if d <188 classify as moderate or ≥188 classify as severe, where d 

represents the cumulative number of egg clusters.

Sampling should be conducted any time after oviposition is complete. To do this a dominant or codominant tree is chosen and d.b.h. is measured. The nearest dominant or co-dominant trees are then 

chosen and their diameter recorded until 20 trees are tallied. The tree with the smallest diameter is then 

used as a base line for sampling successive trees.
54

Table

Table 2 reprinted with permission from the Canadian Entomologist, January 15, 2001.

Table 2. Sequential plan for sampling Swaine jack pine sawfly, Neodiprion swainei Middl., populations based on the 

number of egg clusters per tree.

No. of 

trees

Cumulative number of egg clusters

Light

≤

Continue Sampling

≥

Moderate

≤

Continue Sampling

≥

Severe

1 - - - 83

2 - - - 102

3 2 - - 120

4 7 - - 139

5 12 - - 158

6 17 44 49 177

7 22 49 68 196

8 27 54 86 214

9 33 59 105 233

10 38 64 124 252
55

Hemlock Sawfly

Neodiprion tsugae Middleton 

Hymenoptera: Diprionidae

Hard, J. S. 1971. Sequential sampling of hemlock sawfly eggs in southeast Alaska. Res. 

Note PNW-142. Portland, OR: U.S. Department of Agriculture, Forest Service; 9 p.

Objectives

To classify population densities of N. tsugae quickly and with known confidence, and to enable 

intergeneration comparisons of density.

Abstract

The hemlock sawfly, Neodiprion tsugae Middleton, is an important defoliator of western hemlock, Tsuga 

heterophylla (Raf.), in southeast Alaska. Females insert eggs singly into the edges of hemlock needles in 

the fall. Following a lengthy overwintering period, larvae emerge the following June. The period from 

October through June provides an opportunity for estimating population levels of N. tsugae based on egg 

density.

Neodiprion tsugae egg densities are classified rapidly through examination of branch samples from the 

upper crowns of intermediate-sized western hemlock trees. Branch samples are examined until a single 

egg is found, which reduces greatly the amount of time spent sampling. A tree is classified as infested if 

the sample yields one or more eggs, and uninfested if it yields none. The percentage of infested trees is 

used to classify populations as light (≤33.3% infested) or moderate to heavy (≥50% infested).

Sampling Procedure

Select trees and plots randomly within the area of concern. Climb or fell the nearest intermediate crown 

class western hemlock and remove four 46-cm branch tips from the midpoint of the upper crown. 

Examine each sample for eggs. If a single egg is found, record as infested, discontinue sampling, and 

disregard the rest of the sample from that tree. If after examining all four branches, no eggs are found, 

the tree is classified as uninfested. Using the sequential sampling form (Fig. 3), and beginning at the origin 

of the graph, draw a line up one square for an infested tree or a line right one square for a uninfested 

tree. Discontinue sampling once a decision threshold is crossed (Fig. 3). If a decision is not met, sampling 

should be discontinued at 15 trees and the plot considered a borderline case. Stands are classified as light 

populations if ≤33.3% of sample trees have eggs and moderate to heavy if ≥50% of sample trees have 

eggs.

Notes

Only eggs deposited during the current generation are counted. Eggs from the previous generation, 

which appear brown, may still be found but should not be considered. If the infested area is relatively 

large, a single plot may not be adequate to provide a useful population index.
56

Figure

Figure 3. Hemlock sawfly egg sequential sampling.

0 5 1

0

1

5

2

0

0

5

10

15

20

Continue 

Sampling

Endemic to

Light 

Population

Moderate to 

Heavy

Population

0.0

5

0.0

5

0.1

0

0.1

0

0.2

0

0.2

0

X

Figure 3. Hemlock sawfly egg sequential sampling.

Number of trees with eggs present

Number of trees without eggs

present
57

Larch Casebearer

Coleophora laricella (Hübner) 

Lepidoptera: Coleophoridae

Ciesla, W. M.; Bousfield, W. E. 1974. Forecasting potential defoliation by larch 

casebearer in the northern Rocky Mountains. Journal of Economic Entomology 

67: 47-51.

Objective

To predict defoliation by C. laricella using counts of overwintering larvae.

Abstract

The larch casebearer, Coleophora laricella (Hübner), is an introduced species first recorded in Massachusetts 

in 1886. It now occurs throughout most of the range of larch, Larix spp. Coleophora laricella overwinters as 

third instar larvae within a larval case attached to the base of needle fascicles (i.e., spur shoots) after the 

trees have shed their foliage. Heavy losses result from reduced growth and twig mortality. Trees defoliated 

completely for two or more consecutive years are usually killed.

The quadratic regression model, Y= 4.015+0.4419X-0.001036X2

, for forecasting defoliation potential by 

C. laricella, was developed on western larch (Fig. 4). The model uses counts of overwintering third instar 

C. laricella on 40 branch samples per sample point (X) to forecast feeding injury, which is expressed as 

a numerical rating (Y), and can be translated into negligible, light, moderate, and heavy defoliation. The 

procedure classified defoliation levels correctly on 83% of the sample plots for the first year’s data, and 

64% for the second year’s data. Predictions were within one defoliation class 98% of the time during 

both generations.

Sampling Procedure

Collect four branch samples of at least 100 spur shoots (25 per branch) from the mid-crown of 10 

dominant or codominant western larch with pole pruners (n = 1000). In the laboratory, count the 

number of overwintering third instar larvae on the terminal 100 spur shoots per sample (X). Compare 

this value to the threshold limits in Table 3 to determine the predicted defoliation class as negligible 

(no visible defoliation or discoloration), light (<26% foliage discolored), moderate (26-50% foliage 

discolored) or heavy (>50% foliage discolored).

Notes

Other variables such as elevation, natural enemy abundance, foliage volume, infestation age and climatic 

factors can alter the relationships observed in this study. This study was conducted in natural stands 

of western larch, 9-15 m in height, adjacent to logging roads. The applicability of these results may be 

limited to similar stands.
58

Figure and Table

Table 3 and Figure 4 reprinted with permission from the Journal of Economic Entomology, 

January 15, 2001.

Fig. 4. Curve of larch casebearer defoliation; prediction equation Y = 4.015+0.4419x-0.001036X2, 

based on 2-year data, data vs. curves for the 1970-71 generation (circles) and 1971-72 generation 

(triangles). 

No. overwintering larvae/100

spur shoots (x) Defoliation index (y) Predicted defoliation

0 - 11.5 0 - 8.9 Negligible

11.6 - 60.4 9.0 - 26.9 Light

60.5 - 136.5 27.0 - 44.9 Moderate

136.6 - 236.75 45.0 Heavy

*Highest population density observed.
59

Lodgepole Needle Miner

Coleotechnites milleri (Busck) 

Lepidoptera: Gelechiidae

Stark, R. W. 1952. Sequential sampling of the lodgepole needle miner. Forest Chronicle 

28: 57-60.

Objective

To develop a sequential sampling plan for classifying populations of C. milleri.

Abstract

The lodgepole needle miner, Coleotechnites milleri (Busck), is an important defoliator of lodgepole pine, 

Pinus contorta Dougl., in the western USA. Infestations cause severe growth loss and extensive tree 

mortality as is evidenced in the Ghost Forest of Yosemite National Park. A sequential sampling program 

for estimating C. milleri populations was developed. Four to eight branch tips were sampled from the 

lower and upper crown in each tree until a classification was reached. Populations were classified as either 

light (≤5), medium (15-25), or heavy (≥35 larvae per branch tip). 

Sampling Procedure

Collect an equal number of branch tips from the upper and lower crown of lodgepole pines. Although 

there is no fixed sample size, it is recommended that four to eight branch tips be sampled per tree, taking 

four from the upper crown and four from the lower crown. Cut back branch tips to include 5-year old 

needles and record the number of viable larvae. Continue sampling trees until the cumulative number of 

larvae, when plotted, crosses one of the four lines on the sequential graph (Fig. 1). Error estimates are 

5% for medium and heavy infestations, and 10% for light infestations. 
60

Figure

Figure reprinted with permission from The Forest Chronicle, January 15, 2001. 

Figure 1. Sequential sampling plan for classifying populations of Coleotechnites milleri as light (error 

= 10%), medium or heavy (error = 5%) based on the cumulative number of larvae sampled.
61

Lodgepole Needle Miner

Coleotechnites milleri (Busck) 

Lepidoptera: Gelechiidae

Stevens, R. E.; Stark, R. W. 1962. Sequential sampling for the lodgepole needle miner, 

Evagora milleri. Journal of Economic Entomology 55: 491-494.

Objective

To develop a sequential sampling plan for classifying population densities of C. milleri.

Abstract

The lodgepole needle miner, Coleotechnites milleri (Busck), is an important defoliator of lodgepole pine, 

Pinus contorta Dougl., in the western USA. Infestations cause severe growth loss and extensive tree 

mortality as is evidenced in the Ghost Forest of Yosemite National Park. A sequential sampling program 

is presented, which is based on Stark (1952). This modified plan is designed primarily for use in extensive 

surveys and control operations. It was developed for the sampling of late instar larvae, but may also be 

used for earlier ones. Population were classified as either light (<8), medium (12-22), heavy (26-36) or 

very heavy (>40 larvae per branch tip).

Sampling Procedure

The sampling unit is a branch tip cut back to include only 2-year old needles. Sample 12-15 trees 

(approximately 0.1 ha) randomly and a minimum of 20 branch tips per plot in the area of concern. 

Remove samples with pole pruners from the mid-crown. Once branches are felled, trim the latest two 

years’ infested growth. Count all insects in the field, recording all newly-hatched larvae as alive. As larvae 

develop, differentiating between live and dead larvae is possible by simply tapping the needle. When the 

total number of live larvae falls into one of the decision classes, discontinue sampling (Table 1).

Notes

All pupae are considered alive due to the difficulty in differentiating between live and dead pupae. 

Sampling the last two whorls of foliage gave meaningful estimates of the population in this study, but 

may not hold true elsewhere. The authors suggest preliminary sampling to determine if the population 

is distributed uniformly along the branch. If it appears that the average annual needle production per tip 

deviates heavily from 60, another sequential graph should be made to reflect those differences.

Reference

* Stark, R. W. 1952. Sequential sampling of the lodgepole needle miner. Forest Chronicle 28:57-60.
62

Table

Cumulative number of larvae

No. of 

samples

Light

Light

Continue sampling

Medium

Medium

Medium

Continue sampling

Heavy

Heavy

Heavy

Continue sampling

Very

Heavy

Very heavy

11 1 - - - - 528

15 40 - - - - 680

20 90 310 370 590 650 870

25 140 360 490 710 840 1060

30 190 410 210 830 1030 1250

35 240 460 730 950 1220 1440

Table 1 reprinted with permission of the Journal of Economic Entomology, January 15, 2001.
63

Eastern Hemlock Looper

Lambdina fiscellaria fiscellaria (Guenée) 

Lepidoptera: Geometridae

Dobesberger, E. J. 1989. A sequential decision plan for the management of eastern 

hemlock looper, Lambdina fiscellaria fiscellaria (Lepidoptera: Geometridae), in 

Newfoundland. Canadian Journal of Forest Research 19: 911-916.

Objectives

To develop a sequential sampling plan to aid in monitoring population density; and to crudely forecast 

the amount of defoliation caused by L. fiscellaria fiscellaria.

Abstract

The eastern hemlock looper, Lambdina fiscellaria fiscellaria (Guenée), causes severe defoliation, growth loss, 

and subsequent mortality in balsam fir, Abies balsamea (L.) Mill., stands. The young larvae feed on a variety 

of hosts, but survive best on newly developing balsam fir needles. Older larvae feed indiscriminately, 

and defoliation is usually evident by late July to early August. A sequential sampling plan that classifies 

defoliation levels as light or severe was derived for L. fiscellaria fiscellaria egg populations in Newfoundland, 

Canada.

More eggs were found on mid-crown balsam fir branches than on other sampling substrates, including 

ground mosses, loose bark from white birch, Betula papyrifera Marsh., and crown lichens. An average of 

less than 6 mid-crown branches is required to reach a decision. Infestations were classified as light (<25% 

defoliation; ≤4 eggs per branch) or severe (75% defoliation; ≥10 eggs per branch). 

Sampling Procedure

Remove one mid-crown branch randomly from balsam fir with pole pruners. Soak branches in 2% 

solution of sodium hypochlorite (NaOCl) for 45 min. Agitate the solution vigorously for 5 min then 

filter through a nest of sieves to extract eggs, which were classified as fertile (brown), infertile (green), 

or parasitized (black). The relationship between defoliation and egg density suggests a damage boundary 

of ≤4 eggs per branch for light infestations (<25% defoliation), and ≥10 eggs per branch for severe 

infestations (>75% defoliation). Consult Table 3 for the number of samples required to reach a decision 

(error = 10%). Continue sampling until the cumulative number of eggs reaches or exceeds a decision 

threshold. Defoliation below the lower limit is predicted to be light, while those that exceed the upper 

limit will be severe. No more than six branches should be required to reach a decision.

Note

These data are specific to spruce-fir forests of Newfoundland and may not yield statistically sound 

decisions in other regions.
64

Table

No. of whole branch 

samples

No. of eggsa

Cumulative total eggs Lower limit Upper limit

1 --- 0 19

2 --- 0 25

3 --- 5 32

4 --- 11 38

5 --- 17 44

a

Sampling should be contined until cumulative total eggs exceeds or equals upper limit or is less than 

or equals lower limit. For lower limit, light defoliation is recommended, whereas for upper limit, severe 

defoliation is recommended.

Table 3 reprinted with permission from the Canadian Journal of Forest Research, January 15, 

2001.
65

Eastern Hemlock Looper

Lambdina fiscellaria fiscellaria (Guenée) 

Lepidoptera: Geometridae

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.

Objective

To describe an efficient method of processing moss and bark for sampling L. fiscellaria fiscellaria eggs.

Abstract

The eastern hemlock looper, Lambdina fiscellaria fiscellaria (Guenée), causes severe defoliation, growth loss 

and subsequent mortality in balsam fir, Abies balsamea (L.) Mill., stands. The young larvae feed on a variety 

of hosts, but survive best on newly developing balsam fir needles. Older larvae feed indiscriminately, 

and defoliation is usually evident by late July to early August. Traditional sampling procedures for newlyhatched larvae provide only 2 weeks advance notice for control scheduling. Eggs are present from 

September to June, and could be sampled in fall, thereby providing ample time for planning control 

actions the following spring. Bleach solutions (2, 5, 10, 15, and 20%) were examined for their potential to 

loosen eggs from shredded moss and bark samples.

The stronger solutions (5, 10, 15, and 20%) released eggs quicker than the weakest one, but the eggs 

disintegrated following soaking. A 2% bleach solution bath for 45 minutes will release L. fiscellaria fiscellaria

eggs without deleterious effects. This sampling technique is more efficient and permits egg sampling over 

more extensive areas than the use of direct observations.

Sampling Procedure

Collect moss and bark samples containing L. fiscellaria fiscellaria eggs within the area of concern. Soak 

samples in a 2% bleach solution for 45 min in a mechanical shaker to separate eggs from their substrate. 

Remove large debris and filter the remainder through Number 10 and 40 sieves to collect eggs. Rinse 

screens under running water for 10 min to halt the corrosive action of the bleach. Wash eggs onto filter 

paper, and place under a dissecting microscope to tally. Hand shredding of moss and bark samples into 

smaller pieces increased significantly the number of eggs obtained. Eggs collected by this method can be 

used subsequently to determine egg viability.
66

Western Hemlock Looper

Lambdina fiscellaria lugubrosa (Hulst) 

Lepidoptera: Geometridae

Carolin, V. M.; Johnson, N. E.; Buffam, P. E.; McComb, D. 1964. Sampling egg 

populations of western hemlock looper in coastal forests. Res. Pap. PNW-14. 

Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest 

Research Station; 15 p.

Objectives

To determine the best sampling unit for detecting infestations; to determine preferred locations of egg 

deposition on codominant western hemlock, Tsuga heterophylla (Raf.) Sarg. trees, and on ground sites 

below these trees; and to determine which ground-site samples produce estimates representative of egg 

deposition on the tree.

Abstract

The western hemlock looper, Lambdina fiscellaria lugubrosa (Hulst), is a destructive defoliator that causes 

damage periodically to western hemlock stands and other coniferous hosts. Outbreaks occur every 11-17 

years in coastal areas of the Pacific Northwest. The infestations arise suddenly, persist for 3 years, and 

cause growth reduction, top kill, and tree mortality. Most outbreaks occur in mature and senescing stands 

of western hemlock intermixed with Douglas-fir, Pseudotsuga menziesii (Mirb.) Franc., sitka spruce, Picea 

sitchensis (Bong.) Carr., Pacific silver fir, Abies amabilis (Dougl.) Forbes, and western red cedar, Thuja plicata

Donn.

Potential outbreaks must be detected at an early stage and evaluated so that direct control can be applied 

promptly. Sampling of egg populations has been directed at mossy surfaces accessible from the ground 

or overstory trees, but never both. Studies to determine the distribution of L. fiscellaria lugubrosa eggs on 

both overstory trees and ground sites were conducted to improve sampling techniques for detecting and 

evaluating infestations over large areas.

Sampling Procedure

An in-depth study of possible sampling locations for correlating egg density with defoliation levels is 

discussed. Due to the high costs of tree felling to obtain samples and the high error associated with all 

sampling locations, no procedure is recommended for estimating egg density. However, detection of 

looper infestations may be accomplished by visual examination of mossy log surfaces and bole sections at 

breast height, which were found superior to tree crown units. The strong association between moss and 

egg deposition indicated that egg densities for evaluation purposese should be expressed as the number 

of eggs per square meter of moss.
67

Western Hemlock Looper

Lambdina fiscellaria lugubrosa (Hulst) 

Lepidoptera: Geometridae

Liang, Q.; Otvos, I. S.; Bradfield, G. E. 1996. Distribution pattern and sampling of eggs 

of the western hemlock looper (Lepidoptera: Geometridae) in mature western 

hemlock stands. Journal of Economic Entomology 89: 1531-1536.

Objectives

To determine the vertical distribution of eggs within tree crowns; to determine the optimum sample sizes 

for different error levels; to investigate tree variation related to oviposition; and to develop a sampling 

plan based on egg frequency.

Abstract

The western hemlock looper, Lambdina fiscellaria lugubrosa (Hulst), is a destructive defoliator that causes 

damage periodically to western hemlock, Tsuga heterophylla (Raf.) Sarg., stands and other coniferous hosts. 

Damage occurs in mature and senescing stands where severe defoliation causes growth reduction, top 

kill, and tree mortality. An egg sampling study was conducted in mature western hemlock stands from 

1992-1994 in British Columbia, Canada.

Egg distribution within crowns was homogenous, suggesting that the lower crown was acceptable for 

egg density estimation. The optimal sample size for estimating eggs was presented for error margins 

of 10, 20, 40, 60 and 80% (Table 3). Tree height, d. b. h., crown width, crown length, and presence of 

heartrot did not affect egg lay. Lichen samples (40 g, air dried) were taken from the lower crown. Eggs are 

separated using the method of Otvos and Bryant (1972) for eastern hemlock looper, Lambdina fiscellaria 

fiscellaria (Guenée).

Sampling Procedure

Because random sampling is difficult to conduct in mature hemlock stands, choose dominant or 

codominant trees subjectively. Sample the lower crown for lichen using a pole pruner, and collect enough 

from each tree to fill a polyethylene bag (20 by 10 by 46 cm) loosely. Remove debris from the lichen and 

determine a sample size to use, which is measured in grams of air dried lichen. In this study, 40 g was 

chosen as a sample size because it was the smallest common to all samples collected. Separate eggs from 

the lichen using the method outlined in Otvos and Bryant (1972). Count only eggs of the current year 

as either healthy (brown), infertile (green), or parasitized (black). Refer to Table 3 to determine optimal 

sample sizes based on five levels of error. An error of 20% is generally considered appropriate, and 

would require the sampling of 16 trees if an average of 25 eggs per sample were collected (Table 3).

Note

Restrict sampling to mature western hemlock stands that have an abundance of lichen.

Reference

* 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. 
68

Table

Table 3. Optimum sample size (number of 40 g air-dried lichen samples per plot) for sampling L. 

fiscellaria lugubrosa eggs in mature western hemlock stands in interior of British Columbia, based on data 

of 1992-1994.

Mean

D (95% error margin/mean)

0.1 0.2 0.4 0.6 0.8

1 1316 329 82 36 21

25 63 16 4 2 1

50 37 9 2 1 1

75 28 7 2 1 1

100 24 6 1 1 1

125 21 5 1 1 1

150 19 5 1 1 1

Table 3 reprinted with permission from the Journal of Economic Entomology, January 15, 2001.
69

Western Hemlock Looper

Lambdina fiscellaria lugubrosa (Hulst) 

Lepidoptera: Geometridae

Shore, T. L. 1989. Sampling western hemlock looper pupae (Lepidoptera: Geometridae) 

using burlap traps. Journal of Entomological Science 24: 348-354.

Objective

To develop procedures for using pupae caught in burlap traps as an index of L. fiscellaria lugobrosa density.

Abstract

The western hemlock looper, Lambdina fiscellaria lugubrosa (Hulst), is a destructive defoliator that 

periodically causes damage to western hemlock, Tsuga heterophylla (Raf.) Sarg., stands. Damage generally 

occurs in mature stands where severe defoliation causes growth reduction, top kill, and tree mortality. 

Burlap bands wrapped around western hemlock trees at breast height were used to sample L. fiscellaria 

lugubrosa pupae. The number of pupae was highly variable, and not related to tree diameter or trap surface 

area. There was a significant positive linear relationship between the number of viable pupae per trap 

(X) and the number of healthy eggs (Y) subsequently deposited on lichen in the trees (Y = 0.368X; R2

 = 

0.88, P = 0.017, n = 4). A figure demonstrating the relationship between the mean number of pupae per 

trap and the sample size required with a 20% sampling error was presented.

Sampling Procedure

The number of sample trees required to obtain estimates within 20% of the population mean at low 

densities is large (Fig. 1). Alternatively, sampling to a fixed level of precision, such as ± 10 pupae, is 

acceptable (Fig. 1).

Wrap a 25 cm wide piece of burlap around the bole of each tree at breast height. Secure the band loosely 

to allow larvae to crawl beneath the burlap. Visit trees frequently during the pupation period and remove 

all pupae from beneath each band. Count and record the number of pupae attached to the burlap. A 

positive linear relationship exists between the number of healthy eggs per 100 grams lichen, and the 

number of pupae per burlap trap:

Y= 0.368 X

where, Y is the number of healthy eggs per 100 grams lichen, and X is the number of viable pupae per 

burlap band (R2 = 0.88, P = 0.017, n = 4).

This predictive index appears to overestimate defoliation levels. The author suggests it can be used as 

a predictive index for population density and defoliation estimates the following year, but may require 

modifications as more data become available.

Notes

The number of pupae attached to the burlap was selected as the best estimator since it is less variable 

with respect to the mean, and is the most sensitive indicator at low populations levels. Since there were 

no significant differences among trap surface areas, d.b.h., and the number of pupae caught, it is not 
70

necessary to standardize pupal counts to represent the trap surface area.

Figure

Fig. 1. The relationship between mean number of pupae per trap and the sample size required 

to obtain precision of +20% of the mean of +10 pupae. Based on the variance-mean regression 

S2=4.34X1.59, (R2=0.94), substituted into equations [1] and [2] in text.

Figure 1 reprinted with permission from the Journal of Entomological Science, January 15, 2001.
71

Bruce Spanworm

Operophtera bruceata (Hulst) 

Lepidoptera: Geometridae

Herbert, 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. 

Objective

To develop a useful and efficient sampling method for O. bruceata.

Abstract

The bruce spanworm, Operophtera bruceata (Hulst), is a major defoliator of maple, Acer spp., and aspen, 

Populus spp., stands throughout Canada. Previous sampling methods included the use of sticky bands to 

sample the wingless female, but they were costly and laborious to maintain. A new egg sampling method 

that uses an oviposition trap has been developed. The trap, constructed from a piece of black ABS pipe, 

is placed in the ground and a styrofoam band and lid are attached to facilitate egg laying by O. bruceata. 

The styrofoam band is later removed and then returned to the laboratory for egg counts.

Sampling Procedure

Cut a piece of black ABS pipe, 10 cm in diameter and 1.2 m long, and draw a line 30 cm from one end. 

This line will later indicate the depth the pipe should be driven into the ground. Obtain a 6 mm-thick 

band of styrofoam 10 by 36 cm, and connect the ends to make an opened cylinder using 5-cm wide 

masking tape. Obtain a black cover with a hole drilled in the center for the pipe and a Multi-Pher trap 

lid (Jobin and Coulombe 1988) or similar lid (26.5 cm in diameter). The two lids are attached together 

with a bolt and butterfly nut. The Multi-Pher lid provides a shelter for female insects and protects 

the styrofoam from weathering. Use a wooden mallet and drive the pipe into the ground. Install the 

styrofoam band onto the mounted ABS pipe cover. Install the entire assembly on the post making sure 

that the styrofoam band is supported along the entire periphery of the lid. The masking tape seam should 

face a northern aspect.

Eggs are recovered by removing the styrofoam band and returning them to the laboratory for tally.

Note

For more information contact Dr. Christian Herbert, Canadian Forest Service, Laurentian Forestry 

Centre, 1055 du PEPS, PO Box 3800, Sainte-Foy, Quebec G1V 4C7.

Reference

Jobin, L. J.; Coulombe, C. 1988. The Multi-Pher insect trap. Inf. Leafl. LFC-24E. Saint-Foy, PQ Canadian 

Forest Service, Quebec Region; 8 p.
72

Forest Tent Caterpillar

Malacosoma disstria (Hübner) 

Lepidoptera: Lasiocampidae

Batzer, H. O.; Martin, M. P.; Mattson, W. J.; Miller, W. E. 1995. The forest tent caterpillar 

in aspen stands: distribution and density estimation for four life stages in four 

vegetation strata. Forest Science 41: 99-121.

Objective

To develop a procedure for estimating density of M. disstria. 

Abstract

The 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.

The forest tent caterpillar exemplifies the mobile class of defoliators by expanding its vertical distribution 

during development from the tree canopy to the ground. This study investigated the distribution of 

eggs, small larvae, large larvae, and cocoons in overstory-tree, high-shrub, low-shrub, and ground strata 

in stands of P. tremuloides. All parts of P. tremuloides as well as ground vegetation underneath aspen stands 

were sampled for M. disstria life stages.

Egg mass sampling was the most reliable estimator of population density as larval and pupal counts 

proved to be rather laborious and imprecise. The number of egg masses per tree was estimated from 

samples in the upper and mid-crown, and d.b.h. using a branch model.

Sampling Procedure

Collect the three longest mid-crown branches and the single longest upper crown branch. Record the 

number of egg masses, and diameter at 1.3 m height. Whole tree egg mass numbers can be calculated 

using the equation

EMT = 1.83 UC1

 + [1.48 MC1 +0.91 (MC2 + MC3) + 0.48] (diam.)0.311

where,

EMT = numbers of egg masses in the tree 

UC1 = numbers of egg masses on the longest upper-crown branch 

MC1

 = numbers of egg masses on the longest mid-crown branch 

MC2, MC3 = numbers of egg masses on the second and third longest mid-crown branches

Note

Edge trees should not be sampled since the number of egg masses is usually less than the population 

mean. 
73

Forest Tent Caterpillar

Malacosoma disstria (Hübner) 

Lepidoptera: Lasiocampidae

Connola, D. P.; Waters, W. E.; Smith, W. E. 1959. The development and application of 

a sequential sampling plan for forest tent caterpillar in New York. Bull. No. 366, 

Albany: New York State Museum; 22 p.

Objective

To develop a sequential plan for M. disstria based on egg counts to predict defoliation levels.

Abstract

The forest tent caterpillar, Malacosoma disstria (Hübner), is a major defoliator of hardwood forests in the 

eastern USA and Canada. Young larvae feed on developing buds, while later instars feed gregariously 

on leaves often defoliating the tree completely. Defoliation causes reduced leaf area, growth loss, twig 

dieback and tree mortality in cases of prolonged infestation.

Three methods of obtaining egg mass counts were tested, including direct observation with binoculars, 

a cut-twig method, and whole-tree method where the sample tree was cut down and all branches 

examined for egg masses. The direct observation and whole-tree methods were shown to be ineffective. 

A sequential sampling plan was then developed in response to an outbreak of M. disstria in northern New 

York based on egg counts using the cut-twig method. The purpose of the plan was to predict, from year 

to year, the amount of defoliation simply classified as either noticeable or unnoticeable.

Sampling Procedure

Select 10 (76-cm) twig samples randomly from 25 trees in the area of concern. Sampling preference 

is given to cherry, Prunus spp., and poplar, Populus spp., trees wherever possible, and other susceptible 

species are used only to fill the 25 tree quota, if necessary. Begin by cutting and then examining 10 

twigs from the first sample tree. The number of egg masses obtained will determine whether or not it is 

necessary to cut and examine an additional 10 twigs from another tree by referring to the classification 

thresholds in Table 2. If a decision is not met, take another 10 twig sample, record the cumulative total, 

and continue referencing Table 2 until a decision is made. Defoliation will be classified as unnoticeable or 

noticeable.
74

Table 

TABLE 2 

Sequential plan for sampling forest tent caterpillar egg mass populations in New York.

Sampling guide showing minimum numbers of 10-twig samples that must be taken in an 

egg mass survey to permit site classification with respect to expected forest tent caterpillar 

defoliation.

No. of 10-twig 

sample units

Cumulative total number of egg masses

Number expected to 

produce no noticeable 

defoliation

Range within which the 

amount of defoliation 

expected is doubtful 

(continue sampling if 

count falls in this column)

Number expected to 

produce noticeable 

defoliation

1 - 0-5 6 or more

2 0 1-7 8 “ “

3 2 or less 3-9 10 “ “

4 4 “ “ 5-11 12 “ “

5 6 “ “ 7-14 15 “ “

6 8 “ “ 9-16 17 “ “

7 11 “ “ 12-18 19 “ “

8 13 “ “ 14-20 21 “ “

9 15 “ “ 16-22 23 “ “

10 17 “ “ 18-24 25 “ “

11 20 “ “ 21-27 28 “ “

12 22 “ “ 23-29 30 “ “

13 24 “ “ 25-31 32 “ “

14 26 “ “ 27-33 34 “ “

15 28 “ “ 29-35 36 “ “

16 30 “ “ 31-38 39 “ “

17 33 “ “ 34-40 41 “ “

18 35 “ “ 36-42 43 “ “

19 37 “ “ 38-44 45 “ “

20 39 “ “ 40-46 47 “ “

21 41 “ “ 42-48 49 “ “

22 43 “ “ 44-51 52 “ “

23 46 “ “ 47-53 54 “ “

24 48 “ “ 49-55 56 “ “

25 50 “ “ 51-57 58 “ “

This plan is set up with specified confidence levels. The chances are only 1 in 10 that a “not noticeable” 

area will be called “noticeable” and only 1 in 20 that a noticeable area will be called “not noticeable.” This 

means that 1 out of every 10 areas labeled “noticeable” may show “no noticeable” defoliation and 1 out of 

every 20 areas labeled “not noticeable” defoliation may show “noticeable” defoliation.

Printed with permission from the New York State Museum, Albany, New York.
75

Forest Tent Caterpillar

Malacosoma disstria (Hübner) 

Lepidoptera: Lasiocampidae

Shepherd, R. F.; Brown, C. E. 1971. Sequential egg-band sampling and probability 

methods of predicting defoliation by Malacosoma disstria (Lepidoptera: 

Lasiocampidae). Canadian Entomologist 103: 1371-1379.

Objective

To describe methods for sequential sampling of M. disstria egg masses and predicting defoliation levels 

the following spring. 

Abstract

The forest tent caterpillar, Malacosoma disstria (Hübner), is a major defoliator of hardwood forests. Young 

larvae feed on developing buds, while later instars feed gregariously often defoliating the tree completely. 

Defoliation causes reduced leaf area, growth loss, twig dieback and tree mortality in cases of prolonged 

infestation. A sequential sampling plan for predicting defoliation of trembling aspen, Populus tremuloides

Michx., based on M. disstria egg mass density, is described and its accuracy assessed.

The cumulative egg mass totals from 46-cm branch samples were compared to decision thresholds that 

classify the amount of defoliation expected the following year as light, moderate, or heavy (Table IV). 

The plan achieved an accuracy rate of 65% when comparing predicted with actual defoliation levels the 

following spring. A second method, sequential sampling with sliding boundaries, is a combination of the 

sequential sampling plan but adjusts for the age of the outbreak (Table VIII). This plan improved the 

accuracy rate to 73%.

Sampling Procedure

Sequential sampling plan: Select only dominant and codominant trees, and sample two branches per tree 

from among the top four branches exclusive of the terminal. Record the number of egg masses on the 

first 46 cm of the main branch from the tip. Any lateral shoots initiating within this distance should also 

be examined. Compare the cumulative egg mass totals to the decision thresholds presented in Table IV 

and continue sampling until a decision is met. The amount of defoliation will be classified in one of three 

categories:

Light: No trees exhibit complete defoliation. Feeding damage nonexistent or 

confined to the top of aspen crowns. Little or no feeding evident on other tree 

species or underbrush.

Moderate: The occasional aspen may be completely defoliated, however most have tops 

partially defoliated (thinned). Little feeding on underbrush. 

Heavy: Aspen trees completely defoliated with conspicuous feeding damage 

present to other species including underbrush.
76

This plan achieved a predictive level of 65%, indicating that the number of egg masses found per 

branch in the fall is probably not a consistent predictor of defoliation levels the following spring. This 

discrepancy between expected and actual defoliation increases with age of the outbreak. Forest tent 

caterpillar populations typically suffer lower egg viability and higher larval mortality in late outbreak 

stages than in early ones, which results in less tree defoliation.

Sequential sampling with sliding class boundaries: Sequential sampling with sliding class boundaries 

involves estimating the number of egg masses on a two branch sample rather than simply estimating 

defoliation class. A table of defoliation levels, which is adjusted depending upon the stage of the 

outbreak (i.e., age), is used to classify defoliation as either light-moderate or moderate-heavy (Table VIII). 

Using the new defoliation estimates the predictive level was increased to 73%, and was particularly useful 

during the later stages of the outbreak. It may be advantageous to consult the original publication prior 

to conducting this sampling technique. 

Notes

Defoliation levels during the first year of a M. disstria outbreak can be assessed most accurately with the 

sequential sampling plan, which is critical in preparation of control programs. As the outbreak ages, the 

sequential sampling plan with sliding class boundaries should be used to predict subsequent defoliation 

levels. For the sequential sampling plan with sliding class boundaries, it is assumed that the same 

sequence of yearly defoliation within any local situation will be repeated throughout each outbreak.
77

Tables

Table IV. Sequential table of decision lines from three defoliation levels of aspen associated with 

forest tent caterpillar egg-mass densities.

No. of 

trees

25Accumulated no. of egg masses per 2-branch sample

≤ ≥ ≤ ≥

1

Light

-

Continue Sampling

-

Moderate

-

Continue Sampling

11

Heavy

2 - - - 13

3 - - - 15

4 2 - - 17

5 3 - - 19

6 4 - - 22

7 5 - - 25

8 6 - - 27

9 7 13 13 30

10 8 14 16 32

11 9 15 18 34

12 10 16 20 37

13 11 17 23 39

14 13 18 25 42

15 14 19 28 44

16 15 21 30 46

17 16 22 32 49

18 17 23 35 51

19 18 24 37 53

20 19 25 39 56

21 20 26 42 58

22 21 27 44 61

23 22 28 47 63

24 24 29 49 65

25 25 30 51 68
78

Table VII. Relation between defoliation class boundaries for aspen stnads as denoted by number of egg 

bands of the forest tent caterpillar per two upper branches per tree, and age of outbreak.

Age of outbreak (yr)

Defoliation - class boundary

Light - moderate Moderate- heavy

Eggs bands per two-branch sample

1 1.20 2.40

2 1.55 2.75

3 1.90 3.10

4 2.25 3.45

5 2.60 3.80

6 2.95 4.15

7 3.30 4.50

8 3.65 4.85

Tables reproduced with permission of the Canadian Entomologist, January 15, 2001.
79

Gypsy Moth

Lymantria dispar (L.) 

Lepidoptera: Lymantriidae

Buss, L. J.; McCullough, D. G.; Ramm, C. W. 1999. Comparison of three egg mass 

survey methods in relation to gypsy moth (Lepidoptera: Lymantriidae) 

defoliation in Michigan. Environmental Entomology 28: 485-495.

Objectives

To compare and contrast three egg mass survey methods (fixed-radius plots, timed walks, and 100-tree 

plots); and to correlate each of these methods with defoliation levels.

Abstract

The gypsy moth, Lymantria dispar (L.), was introduced into Medford, Massachusetts in 1869, and is now a 

major defoliator of hardwoods throughout the northeastern USA and Canada. Defoliation reduces tree 

growth and vigor, and in combination with other stress factors, can cause excessive tree mortality. Fixedradius plots, timed walks, and a 100-tree plot were used to assess egg mass density and predict defoliation 

levels in oak, Quercus spp., stands. Egg mass counts from the fixed-radius plot and 100-tree plot methods 

were correlated positively with subsequent defoliation, but timed walks were not. Treatment decisions 

based on fixed-radius plots and timed walks were similar. However, the 100-tree plot method yielded 

fewer erroneous classifications. All stands that sustained greater than 30% defoliation had greater than 

84% new egg masses and a density of at least 6,583 egg masses per hectare.

Sampling Procedure

Fixed-radius plots: Examine all trees within a circular 100-m2

 plot for egg masses, scanning the entire tree 

with binoculars. Logs and large branches should be overturned in search of egg masses. The average 

time needed to conduct the sample is 18.1 min.

Timed walks: Choose a random direction and starting point to establish a transect, and tally the number 

of egg masses along that transect for 5 min. Repeat the count along the transect back to the starting 

point and calculate the mean. The average time needed to conduct the sample is 10 min.

100-tree plots: Begin at plot center and walk in a circular path of increasing diameter until 100 trees 

(>4 cm d.b.h.) have been examined. Count the number of new egg masses on the lower 2 m of each 

tree bole, measuring the length along the longest axis of the first 10-15 egg masses encountered and 

computing the mean. To determine the ground area of each 100-tree plot, add the N-S and E-W radii, 

and then multiply to determine the area of each 100-tree plot. Allow 30 minutes to collect the sample. 

This method is most accurate at predicting defoliation levels and is, therefore, recommended for use. 

A two-step protocol has been developed for decision-making based on the 100-tree plot. Control is 

recommended if the mean number of new egg masses per tree is greater than 2.0, and not recommended 

if this value is less than 0.2. When the mean is between the two values, use the following equation:

Defoliation class = 0.458 * %Oak + 0.744 * EML + 0.432* ratio
80

where, EML is the mean length of new egg masses (inches) and ratio is the ratio of new to old egg 

masses ((new egg masses + 10)/ (old egg masses + 10)). A defoliation class of 2 represents 37.5% 

defoliation and is used as the decision cutoff for recommending control.

Note

Defoliation classes and subsequent control recommendations are based on the number of new egg 

masses.
81

Gypsy Moth

Lymantria dispar (L.) 

Lepidoptera: Lymantriidae

Carter, J. L.; Ravlin, F. W.; Gray, D. R.; Carter, M. R.; Coakley, C. W. 1994. Foliage 

presence and absence effect on gypsy moth (Lepidoptera: Lymantriidae) egg 

mass sample counts and the probability of exceeding action thresholds with 

foliage present. Journal of Economic Entomology 87: 1004-1007.

Objective

To determine if there is a significant difference between egg mass estimates taken when foliage is present 

(summer) or absent (winter), and to determine the probability of exceeding action thresholds for summer 

counts based on winter data.

Abstract

The gypsy moth, Lymantria dispar (L.), was introduced into Medford, Massachusetts in 1869, and is now 

a major defoliator of hardwoods throughout the northeastern USA and Canada. Defoliation reduces 

tree growth and vigor, and in combination with other stress factors can cause excessive tree mortality. 

Egg mass sampling is the primary method of estimating populations in order to make control decisions. 

Fixed-and-variable-radius plot samples were taken when foliage was present (summer) and absent (winter) 

at 136 sites, and their relationship analyzed with nonparametric statistics.

Winter counts were 14–36% higher than summer egg counts. The probability of summer egg mass 

counts exceeding action thresholds was determined by fitting a logistic curve to empirical data for 

thresholds of 618 and 1,236 egg masses per hectare. If egg mass samples are taken when foliage is 

present, then the data needs to be adjusted for differences between summer and winter counts. 

Sampling Procedure

Ninety-seven fixed and variable-radius plots (BAF 20) were established on a 50-m grid in two 9-ha study 

areas in Shenandoah National Park, Virginia. The sampling method is described in detail in Wilson and 

Fontaine (1978). Summer egg mass sampling began after male moth flight had ceased and continued 

through September. Winter egg mass samples were taken after leaf abscission through February. 

The number of egg masses per hectare was calculated for summer and winter counts for each plot. 

Summer egg mass counts were grouped into intervals of 200 (excluding zero), and the frequency 

distribution of winter counts was determined. For each interval, the cumulative frequency in which 

winter counts were above each of the action thresholds of 618 and 1,236 egg masses per hectare was 

used to construct a probability curve (Fig. 1).

Winter counts (W) were significantly higher than summer counts (S) on a per tree basis (W = 1.18 * S; X2

= 129.03, df = 79, P = 0.0003) and plot basis (W = 2.28 + 1.23 * S; X2 = 635.48, df = 136, P = 0.0001). 

Use Fig. 1 to determine the probability that summer counts will exceed an action threshold based on 

winter counts. For example, given an action threshold of 618 egg masses per hectare and a summer count 

of 200 egg masses per hectare, there is only a 31% chance that a winter count would exceed the action 

threshold (Fig. 1). If 600 egg masses were counted per hectare, there is a 93% chance that a winter count 
82

would exceed the action threshold.

Note

The authors suggest there is error in sampling and predation of egg masses, which can result in lower 

winter counts even though greater visibility of egg masses occurs when foliage is absent. 

Reference:

* 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.

Figure

Figure 1 reprinted with permission from the Journal of Economic Entomology, January 15, 2001.

Fig. 1. Probability of a summer egg mass per hectare count for a plot exceeding the action thresholds 

of 618 egg masses per hectare () or 1,236 egg masses per hectare (). Observed probabilities of 

a summer egg mass count exceeding an action threshold are included for both action thresholds. 

Logistic curve equation for action threshold of 618 egg masses per hectare is {1 + exp[-0.00846*(X

- 296.21)]}-1/1.02 and 1,236 egg masses per ha is {1 + exp[-0.00344*(X - 843.44)]}-1/0.992 where X is a 

summer egg mass count.
83

Gypsy Moth

Lymantria dispar (L.) 

Lepidoptera: Lymantriidae

Carter, J. L.; Ravlin, F. W. 1995. Evaluation of binomial egg mass sampling plans for 

low density gypsy moth populations in continuously forested habitats. Journal of 

Economic Entomology 88: 890-896.

Objective

To develop a useful binomial sampling method for estimating low density populations of L. dispar.

Abstract

The gypsy moth, Lymantria dispar (L.), was introduced into Medford, Massachusetts in 1869, and is now a 

major defoliator of hardwoods throughout the northeastern USA and Canada. Defoliation reduces tree 

growth and vigor, and in combination with other stress factors can cause excessive tree mortality. The use 

of binomial sampling for low-density (<618 egg masses per hectare) populations was examined. Fixedand variable-radius plot egg mass samples were also collected at 28 locations (Wilson and Fontaine 1978). 

A model was fit to the egg mass density and the proportion of trees with zero egg masses. Binomial 

sampling plans were developed for sample sizes of 9, 16, 49, and 98 and compared with fixed- and 

variable-radius plot samples.

The binomial method was more efficient than the fixed- and variable-radius plot methods; however, the 

precision of sample sizes was unacceptable. Binomial sampling was not an effective sampling method for 

low density populations of L. dispar.

Sampling Procedure

The fixed- and variable-radius sampling unit consists of variable-radius plot (BAF 20) for sampling 

overstory trees and a fixed-radius plot of 20 m2

 for sampling understory plants. The technique is 

described in detail by Wilson and Fontaine (1978).

Binomial sampling is an efficient sampling technique whereby the presence or absence of an insect 

is used to estimate population density. The plan is based on the relationship between the proportion 

of trees (PT) with zero egg masses and the population mean. Once the relationship is established, a 

population mean can be estimated efficiently for any observed value of PT = 0. Binomial sampling 

significantly reduced the amount of time required for sampling L. dispar egg masses. However, the high 

variability associated with this technique outweighs any savings in time. The continued use of the fixedand variable-radius plot method (Wilson and Fontaine 1978) for sampling low-density populations is 

recommended. 

Reference

* 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.
84

Gypsy Moth

Lymantria dispar (L.) 

Lepidoptera: Lymantriidae

Fleischer, S. J.; Ravlin, F. W.; Reardon, R. C. 1991. Implementation of sequential 

sampling plans for gypsy moth (Lepidoptera: Lymantriidae) egg masses in 

eastern hardwood forests. Journal of Economic Entomology 84: 1100-1107.

Objective

To develop a sequential sampling plan for rapid classification of L. dispar populations.

Abstract

The gypsy moth, Lymantria dispar (L.), was introduced into Medford, Massachusetts 1869, and is now a 

major defoliator of hardwoods throughout the northeastern USA and Canada. Defoliation reduces tree 

growth and vigor, and in combination with other stress factors can cause excessive tree mortality. Sample 

units (timed walks and fixed-radius plots) used for determining egg mass density were evaluated for use in 

area-wide integrated pest management (IPM) programs. Sequential sampling plans based on fixed-radius 

plots (Table 3) were validated within 131 1-km cells. The sequential plans gave the same pest management 

decisions as fixed-sample size plans in 79-84% of the cells, recommended additional samples in 7-19% of 

the cells, and gave incorrect decisions in 2-3% of cells.

Sampling Procedure

Due to the instability of the regression coefficients relating timed walks to fixed-plot data, 100-m2

 plots 

were used. Take a minimum of 4 and maximum of 10 plots in which you locate and record the number 

of egg masses found on all trees, rocks, and in the understory. Use binoculars to examine taller objects, if 

necessary. Continue sampling until a decision is met for one of the three management thresholds (Table 

3).

The sequential plans used fewer samples and yielded correct decisions in 74-96% of the 1-km cells tested. 

Incorrect decisions did not occur in any cells below a treatment threshold using plan 3 or in any cells 

above treatment threshold using plan 5 (Table 3).

Notes

Area-wide IPM programs have a wide range of thresholds, acceptable errors, and resources allocated 

for sampling efforts. No single plan can be expected to be useful throughout a large project. The plans 

presented in Table 3 are a conservative pest management decision-making tool based on meetings with 

managers of southern Appalachian hardwood forests.
85

Table

 Table 3 reproduced with permission of the Journal of Economic Entomology, January 15, 2001.

Table 3. Sequential sample plan parameters using a negative binomial distribution with kc=1.1 for 0.01 ha 

fixed-radius plot sample units developed interactively with field managers using a computer spreadsheet 

for use in a large area IPM project.

Plan no. α β Density 

threshold 

(egg 

masses/0.01 

ha)

Lower 

limit (egg 

masses/0.01 

ha)

Upper 

limit (egg 

masses/0.01 ha

n range Stop linea

Intercept Slope

1b 0.25 0.25 49 0.3 0.7 4-10 1.843 0.464

2 0.25 0.25 49-124 0.3 1.5 4-10 1.109 0.688

3 0.25 0.25 618 5 7.5 4-10 17.722 6.095

4 0.25 0.25 1236 10 15 4-10 32.706 12.178

5 0.25 0.25 2471 21 29 4-10 79.461 24.580

6 0.25 0.05 25 0.2 0.3 21-50 1.279 0.246

a

The positive value of the intercept gives the upper stop line and the negarive value gives the lower stop 

line. 

b

Plan 1 was not used alone but in combination with plans 3, 4, or 5 to make plans that classify 

populations into three categories.
86

Gypsy Moth

Lymantria dispar (L.) 

Lepidoptera: Lymantriidae

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.

Objective

To develop a sequential sampling plan for classifying egg mass density into three categories for two land 

classifications. 

Abstract

The gypsy moth, Lymantria dispar (L.), was introduced into Medford, Massachusetts in 1869, and is now 

a major defoliator of hardwoods throughout the northeastern USA and Canada. Defoliation reduces 

tree growth and vigor and in combination with other stress factors can cause excessive tree mortality. A 

sequential sampling plan was developed for estimating L. dispar egg mass density in forested and urbansuburban habitats. Sequential sampling decision plans for three egg density thresholds (250, 500, and 

1000 eggs per acre) are presented and are based on a 100-m2

 sample plot.

Sampling Procedure

Forested habitats: Take a minimum of 4 and maximum of 10 plots, in which you locate and record the 

number of egg masses found on all plants and rocks in the understory. Use binoculars to examine taller 

objects, if necessary. The sum of samples is compared to a range of values for the corresponding sample 

number in the table for each management threshold selected. Continue sampling until a decision is met 

for one of the three management thresholds (Table 2).

The plan gives the same pest management decision in 79-84% of the areas that were surveyed using 

current operational techniques. This rate of success is achieved with a labor savings of 40%. Only 2-3% 

of the areas were classified improperly.

Urban and suburban habitats: A suburban habitat is defined as an area with a minimum of one house per 

4 hectares. In these environments, the influence of man-made objects causes egg mass distributions to be 

aggregated. The sequential sampling plan is presented in Table 3. Take a minimum of six 100-m2

 (1/40 

acre) plots where you locate and record the number of egg masses found on all trees, rocks, and manmade objects. Use binoculars to examine taller objects, if necessary. The sum of samples is compared 

to a range of values for the corresponding sample number in the table for each management threshold 

selected. Continue sampling until a decision is met for one of the three management thresholds. 

Appendix 1 provides the equations used to generate the sequential plan tables.

The plan gives the same pest management decision in 90-100% of the areas that were surveyed by using 

current operational techniques. This rate of success was achieved with a labor savings of 49%. Only 6% 

of the areas were classified improperly.
87

Note

The frequency distribution and damage thresholds must be known as the frequency distribution is used 

to estimate the probability of being above or below the threshold.

Tables

Table 2. Sequential sampling decision charts for three managemnet thresholds in continuously forested 

eastern hardwoods.

Threshold (egg 

masses/acre)

Sample number 

(1/40 acre)

Stop sampling 

(below threshold)

Continue 

sampling

Stop sampling 

(above threshold)

. . . . Cumulative Egg Mass Count . . . . 

250 4 0-6 7-42 >42

5 0-12 13-48 >48

6 0-18 19-54 >54

7 0-24 25-60 >60

8 0-30 31-66 >66

9 0-36 37-73 >73

500 4 0-15 16-81 >81

5 0-27 28-94 >94

6 0-39 40-106 >106

7 0-52 53-118 >118

8 0-64 65-130 >103

9 0-76 77-143 >143

1000 4 0-18 19-178 >176

5 0-43 44-202 >202

6 0-67 68-227 >227

7 0-92 93-252 >252

8 0-116 117-276 >276

9 0-141 142-301 >301
88

Table 3. Sequential sampling decision charts for three management thresholds in 

urban/suburban habitats.

Threshold (egg 

masses/acre)

Sample number 

(1/40 acre)

Stop sampling 

(below threshold)

Continue 

sampling

Stop sampling 

(above threshold)

. . . . Cumulative egg mass . . . .

250 6 0-3 4-71

7 0-9 10-77

8 0-15 16-83

9 0-21 22-89

10 0-27 28-95

11 0-33 34-101

12 0-39 40-107

13 0-45 46-113

14 0-51 52-119

15 0-57 58-125

500 7 0-6 7-159

8 0-18 19-171

9 0-30 31-182

10 0-42 43-194

11 0-53 54-206

12 0-65 66-218

13 0-77 78-229

14 0-89 90-241

15 0-100 101-253

16 0-112 113-265

17 0-124 125-277

18 0-136 137-288

19 0-148 149-300

20 0-159 160-312

21 0-171 172-324

22 0-183 184-335

1000 7 0-10 11-334

8 0-34 35-359

9 0-59 60-383

10 0-84 85-408

11 0-108 109-432
89

Threshold (egg 

masses/acre)

Sample number 

(1/40 acre)

Stop sampling 

(below threshold)

Continue 

sampling

Stop sampling 

(above threshold)

12 0-133 134-457

13 0-158 159-482

14 0-182 183-506

15 0-207 208-531

16 0-231 232-555

17 0-256 257-580

18 0-280 281-604

19 0-305 306-629

20 0-329 330-654

21 0-354 355-678

22 0-379 380-703

23 0-403 404-727

24 0-428 429-752

Appendix 1. Equations used to generate sequential sampling plans for 1/40th acre fixed plot samples in 

various habitats.

Threshold (EM/acre)

Decision Stop Linea

Continusously Forested Habitat Urban/Suburban Habitatb

250 y = 6.095x ± 17.722 y = 6.089x ± 34.013

500 y = 12.178x ± 32.706 y = 11.781x ± 76.258

1000 y = 24.580x ± 79.461 y = 24.576x ± 162.128

a Decision stop line in the form y = mx ± b, where y = cumulative sum required to stop sampling, m is 

the slope, x is the sample number and b is the intercept. The positive value of the intercept gives the upper stop line and the negative value gives the lower stop line.

b Defined as ≥ 1 house per ten acres.
90

Gypsy Moth

Lymantria dispar (L.) 

Lepidoptera: Lymantriidae

Gansner, D. A.; Herrick, O. W.; Ticehurst, M. 1985. A method for predicting gypsy moth 

defoliation from egg mass counts. Northern Journal of Applied Forestry 2: 78-79.

Objective

To develop a method of predicting defoliation from egg mass counts.

Abstract

The gypsy moth, Lymantria dispar (L.), was introduced into Medford, Massachusetts in 1869, and is now 

a major defoliator of hardwoods throughout the northeastern USA and Canada. Defoliation causes 

reduced growth, decreased vigor and extensive tree mortality. A model is provided which correctly 

predicted defoliation levels as greater or less than 60% for 92% of the 300 plots analyzed. 

Sampling Procedure

Establish two 100-m2 plots throughout the stand. No recommendations as to the spacing of plots is 

provided. Within each plot, locate and record the number of newly deposited egg masses on trees, rocks, 

stumps, etc. Multiply the median of the two plots by 40 to estimate the number of egg masses per acre. 

The equation for predicting defoliation based on egg mass counts is: 

Percent defoliation = 100[1.0 + 7.24 (0.368)0.00173X]

-1

where, X represents the number of egg masses per acre (Table 1).

Table

Figure 1 reprinted from the Northern Journal of Applied Forestry Vol. 2 (p. 78-79) published by 

the Society of American Foresters, 5400 Grosvenor Lane, Bethesda, MD 20814-2198. Not for 

further reproduction
91

Gypsy Moth

Lymantria dispar (L.) 

Lepidoptera: Lymantriidae

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.

Objectives

To evaluate the precision and costs of several sampling procedures; and to develop sampling plans for 

estimating L. dispar egg mass (GMEM) density with constant-precision levels.

Abstract

The gypsy moth, Lymantria dispar (L.), was introduced into Medford, Massachusetts in 1869, and is now 

a major defoliator of hardwoods throughout the northeastern USA and Canada. Defoliation results in 

reduced growth, decreased vigor, and extensive tree mortality. Plots of 100-m2

, 400-m2, 10-prism BAF, 

and 20-prism BAF were evaluated in relation to their efficiency for sampling gypsy moth egg masses 

in 14 oak, Quercus spp., woodlots in Maryland. The reliability of each sampling method varied with egg 

mass density. There was no significant difference in precision between 10 and 20 BAF plots. However, 

the 20 BAF plot required 40% less time to survey than the 10 BAF. An analysis of cost and precision 

showed that 100-m2 plots were superior to the other sampling methods throughout the range of egg 

mass densities evaluated. The total number of new egg masses per plot was determined and compared to 

three sequential sampling plans. Sampling was continued until a decision was met, and populations were 

classified relative to three critical densities as either 50, 617, or 2469 egg masses per hectare.

Sampling Procedure

Sampling 100-m2

 plots was found to yield greater precision per unit sample time than the 400-m2 or BAF 

plots. Therefore, sequential sampling plans were developed based on this sample unit.

Establish 100-m2

 plot centers randomly within the woodlot to be sampled. Count and record all new egg 

masses, and consult one of the three sequential sampling plans based on three different critical values 

for classifying densities as either less or greater than 50, 617, 2,469 egg masses per hectare (Table 4). 

Continue sampling a minimum of three times or until a decision is met. If the observed total falls outside 

the critical values, a decision to treat or not to treat is made.

Note

Site and stand characteristics associated with other geographic locations may alter the relationships 

observed in this study.
92

Table

Table 4. Sequential sampling table for classifying GMEM infestations relative to three critical 

densities.

No. of 

samples

Cumulative GMEM

<50 >50 <617 >617 <2469 >2469

5 —

Continue Sampling

13

Continue Sampling

—

Continue Sampling

71

Continue Sampling

39

Continue Sampling

211

10 — 20 8 118 135 365

15 — 26 27 161 237 513

20 — 32 48 202 342 658

25 — 37 70 242 449 801

30 — 42 93 282 558 942

35 — 46 117 320 669 1081

40 — 51 141 359 780 1220

45 — 55 166 396 892 1358

50 — 59 191 434 1005 1495

55 — 63 216 471 1118 1632

60 — 67 242 508 1232 1768

Table 4 reprinted with permission from Environmental Entomology, January 15, 2001.
93

Gypsy Moth

Lymantria dispar (L.) 

Lepidoptera: Lymantriidae

Liebhold, A. M.; Elkinton, J. S. 1988. Techniques for estimating the density of late-instar 

gypsy moth, Lymantria dispar (Lepidoptera: Lymantriidae), populations using 

frass drop and frass production measurements. Environmental Entomology 17: 

381-384.

Objective

To develop procedures for estimating L. dispar larval densities based on frass drop.

Abstract

The gypsy moth, Lymantria dispar (L.), was introduced into Medford, Massachusetts in 1869, and is now 

a major defoliator of hardwoods throughout the northeastern USA and Canada. Defoliation results in 

reduced growth, decreased vigor and extensive tree mortality. A technique was developed for estimating 

larval densities using measurements of the amount of frass produced per larva (frass yield), and the 

amount of frass falling in the forest per unit area (frass drop). The technique was tested in a post-season 

experiment in which 6,000 larvae were released in a stand. Frass yield was measured by caging larvae 

individually in the field on cut host foliage. The most reliable and efficient method of measuring frass 

drop was the deployment of several large funnel-shaped frass traps (16 cm diameter) on wooden stakes. 

Number of pellets was found to be superior to frass weight as a unit for quantifying frass yield and drop, 

because it was not influenced strongly by instar distribution. The distribution of frass widths suggest 

frass size can be used as a tool to differentiate among instars.

Sampling Procedure

Several traps were evaluated for their effectiveness at collecting L. dispar frass: (1) an 82 by 82-cm canvas 

tarp stretched across a wooden frame and placed on the forest floor; (2) a 63 by 63-cm cheesecloth 

stretched across a wooden frame and placed on the forest floor; (3) a 16-cm diameter plastic disk with an 

acrylic sticker attached to a wooden stake; (4) the disk in (3) with cylindrical sheet metal sides to prevent 

frass bouncing; and (5) a funnel. The funnel trap consists of a polyethylene funnel (16 cm diameter) 

inserted into a section of tygon tubing (1.0 cm diameter by 5 cm long) with mosquito netting glued over 

the bottom. Place the trap on top of a 1 m wooden stake positioned vertically in the ground. Count 

and remove the number of frass pellets every 5 d. The pellets are characteristically star shaped in cross 

section, and distinguished easily from other forest insects. The equation for calculating larval density for 

each day is:

Larvae/ha = C · frass/trap · larvae/frass

where, C = 1/area (ha) of one trap, and C is determined from measuring the horizontal area of the trap. 

The mean amount of frass per trap is determined by the frass/trap (frass drop) term. Density estimates 

based on the number of frass pellets provide more reliable estimates than frass weight.
94

Frass size is a useful tool to determine the abundance of each instar at any given stage after the third 

instar. The diameter ranges of frass pellets for the different instars are as follows:

Instar Frass Diameter (mm)

Fourth 1.0-1.5

Fifth 1.5-2.1

Sixth 2.1-3.0

Note 

Because frass diameters may vary with host quality or population density (Lance and others 1986), 

determination of instars by this method should be done with caution.

Reference

Lance, D. R.; Elkinton, J. S.; Schwalbe, C. P. 1986. Components of density-related stress as potential determinants 

of population quality in the gypsy moth (Lepidoptera: Lymantriidae). Environmental Entomology 15: 914-

918.
95

Gypsy Moth

Lymantria dispar (L.) 

Lepidoptera: Lymantriidae

Liebhold, A.; Thorpe, K.; Ghent, J.; Lyons, D. B. 1994. Gypsy moth egg mass sampling 

for decision-making: a user’s guide. NA-TP-04-94. Morgantown, WV: U.S. 

Department of Agriculture, Forest Service, Northeastern Area; 12 p.

Objectives 

To provide detailed procedures for estimating L. dispar egg mass density and to predict defoliation levels 

using a density-defoliation relationship.

Abstract

The gypsy moth, Lymantria dispar (L.), was introduced into Medford, Massachusetts in 1869, and is now 

a major defoliator of hardwoods throughout the northeastern USA and Canada. Defoliation reduces 

tree growth and vigor, and in combination with other stress factors can cause excessive tree mortality. 

This guide provides detailed information on procedures used for estimating egg density, which is used 

traditionally in decision-making. A 100-m2 plot was recommended for determining egg mass densities, 

which were used to predict the severity of defoliation levels the following year. The relationship between 

egg mass density and subsequent defoliation for three damage criteria are presented (Fig. 8). The control 

thresholds for noticeable defoliation, growth loss and tree mortality were 500-750, 700-900, and 1,000-

1,400 egg masses per acre, respectively. 

Sampling Procedure

There are three methods used widely for estimating egg mass density: fixed and variable radius plots 

(Wilson and Fontaine 1978), and timed walks (Buss and others 1999). However, fixed radius plots are 

recommended for use because they provide similar levels of precision as variable radius plots and timed 

walks, while having reduced sampling cost.

Identify the boundaries of the potential treatment block where egg mass density is to be estimated. 

Determine the number of samples for the desired level of precision (Fig. 1), and delineate sample points 

on the map. Establish a 100-m2 circular plot, and locate and record all of the egg masses present within 

its boundaries. Use binoculars to sample the taller trees if necessary, and make sure to look for egg 

masses under rocks, logs, etc. Determine the proportion of new egg masses in the understory. Multiply 

the number of new egg masses in the plot by 40 to obtain an estimate of egg mass density per acre. The 

mean density of the entire block is estimated from the mean of the plots within it.

There is a positive relationship between defoliation (Y) and egg mass density (X) (Fig. 7). Locate the 

estimated egg mass density on the horizontal axis and read the corresponding predicted defoliation levels 

off the vertical axis. There was much variability in this relationship particularly between 100-1000 egg 

masses per acre where defoliation levels ranged between 0 and 100%. In these situations, it is helpful to 

determine if the population is increasing using such factors as proximity to the leading edge, egg mass 

length (>30 mm), and proportion of new egg masses (>75%).
96

Figure 8 shows the relationship between egg mass density and subsequent defoliation for three damage 

criteria. The following control thresholds are provided:

Defoliation and Damage Egg density per acre

Noticeable (>30%) 500-750

Growth loss (>40%) 700-900

Tree mortality 1,000-1,400

Note

The procedures described here represent a scientifically based approach to decision-making. However, it 

is impossible to predict defoliation levels without a certain amount of error.

References

* Buss, L. J.; McCullough, D. G.; Ramm, C. W. 1999. Comparison of three egg mass survey methods in relation to 

gypsy moth (Lepidoptera: Lymantriidae) defoliation in Michigan. Environmental Entomology 28: 485-495.

* 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

Figures

Figure 1. Minimum number of fixed radius samples (plots) necessary to achieve various levels of precision at different densities. Error is expressed as a percentage of estimated density.
97

Figure 8. Relationship between defoliation and egg mass density thresholds for three damage criteria.
98

Gypsy Moth

Lymantria dispar (L.) 

Lepidoptera: Lymantriidae

Liebhold, A. M.; Elkinton, J. S.; Zhou, G.; Hohn, M. E.; Rossi, R. E.; Boettner, G. H.; 

Boettner, C. W.; Burnham C.; McManus, M. L. 1995. Regional correlation of 

gypsy moth (Lepidoptera: Lymantriidae) defoliation with counts of egg masses, 

pupae, and male moths. Environmental Entomology 24: 193-203.

Objective 

To compare three L. dispar census methods, and determine their ability to predict regional defoliation 

levels.

Abstract 

The gypsy moth, Lymantria dispar (L.), was introduced into Medford, Massachusetts in 1869, and is now 

a major defoliator of hardwoods throughout the northeastern USA and Canada. Defoliation results in 

reduced growth, decreased vigor and extensive tree mortality. 

Three different L. dispar sampling techniques were compared for their spatial correlation with regional 

defoliation maps. Counts of pupae and egg masses under burlap bands, and counts of male moths 

in pheromone-baited traps, were taken in a network of 150 plots distributed irregularly throughout 

Massachusetts. These counts were compared with aerial sketch maps of L. dispar defoliation collected 

during the same period. Egg mass and pupal counts were correlated positively with subsequent 

defoliation. These results indicate that counts of egg masses (or pupae) under burlap bands may be the 

most suitable measure for predicting L. dispar defoliation on a regional scale.

Sampling Procedure

Place a standard milk carton pheromone (50 +:- disparlure) trap surrounded by 20 oak, Quercus spp., trees 

>13 cm d.b.h., where burlap bands are placed. Establish traps in early summer and revisit in late summer 

and early fall. Count all egg masses and pupal remains under the burlap bands, and the number of male 

moths per pheromone trap.

If egg mass counts are greater than 1.8 per tree, or pupal counts are greater than 4 per tree then 

defoliation is likely to occur. The authors suggest that defoliation predictions can be extrapolated, with 

some caution, out to 10 km from where counts are made. For operational use, establish permanent plots 

on a grid network with less than 10 km between plots.

Notes 

Egg mass counts under burlap bands may be more useful than pupal counts because they usually remain 

intact providing a census over several months.

 
99

Gypsy Moth

Lymantria dispar (L.) 

Lepidoptera: Lymantriidae

Liebhold, A.; Luzader, E.; Reardon, R.; Roberts, A.; Ravlin, F. W.; Sharov, A.; Zhou, G. 

1998. Forecasting gypsy moth (Lepidoptera: Lymantriidae) defoliation with a 

geographical information system. Journal of Economic Entomology 91: 464-472.

Objective 

To describe quantitatively the spatial relationship of L. dispar infestations in order to improve the quality 

of defoliation forecasts. 

Abstract 

The gypsy moth, Lymantria dispar (L.), was introduced into Medford, Massachusetts in 1869, and is now 

a major defoliator of hardwoods throughout the northeastern USA and Canada. Defoliation reduces 

tree growth and vigor, and in combination with other stress factors can cause excessive tree mortality. A 

maximum likelihood estimation procedure was used to fit 15 regression models that predict noticeable 

defoliation in 1 ha grids from egg mass density, male moths, presence of defoliation in the previous year, 

distance to the population boundary, and their interaction.

Models that included egg mass density and distance to the population boundary provided the most 

reliable predictions of defoliation. Decision errors were greatest for models that incorporated a single 

independent variable. Models 1, 5 and 11 yielded the lowest decision errors and included egg mass density 

as an independent variable. The results indicated that the use of traditional egg mass density thresholds 

may perform as well or better than the logistic models with the exception of models 5 and 11. However, 

the associated errors for all models are rather high and improved techniques for predicting defoliation 

were, therefore, recommended.

Sampling Procedure 

Data on defoliation levels, egg mass densities, pheromone trap catches, and proximity to the population 

boundary were obtained from infestations in Virginia and West Virginia, and were used to construct and 

validate model accuracy.

Gypsy moth traps were deployed annually on 2 by 2-km grids and baited with disparlure (Schwalbe 1981). 

At the end of the season, the number of moths was recorded in each trap. If greater than 200 moths 

were caught, egg mass densities were sampled using three to ten 100-m2 plots (Liebhold and others 1994). 

Values were estimated for unsampled locations as weighted means of values in nearby locations. Distance 

to the population boundary was determined using the best classification method (Sharov and others 

1995).

Fifteen logistic regression equations were used to predict the probability of defoliation as >30%. All 

possible combinations of the four variables were evaluated. All coefficients were found significant 

and made biological sense (refer to original publication). Decision errors were greatest for models that 
100

incorporated only one independent variable. Models 1, 5, and 11 yielded the lowest decision errors, and 

include egg mass density as an independent variable. Results indicate that the use of traditional egg mass 

density thresholds may perform as well or better than the logistic models with the exception of models 

5 and 11. Both models include egg mass density and distance to population boundary variables. The 

capture of male moths was a poor indicator of subsequent defoliation.

Notes

 

The authors provide a detailed example of two scenarios to illustrate the choice of an egg mass density 

threshold for use in decision-making, and then discuss clearly the effect of each error. The data used 

in these models were collected by the Appalachian Integrated Pest Management program (AIPM) at the 

leading edge of L. dispar advancement. Therefore, these models may not be applicable in other locations 

(i.e., New England) where populations have been established, and variables such as proximity to population boundary are unlikely to be useful indicators of defoliation levels.

References

* Liebhold, A.; Thorpe, K.; Ghent, J.; Lyons, D. B. 1994. Gypsy moth egg mass sampling for decision-making: 

a user’s guide. NA-TP-04-94. Morgantown, WV: U.S. Department of Agriculture, Forest Service, 

Northeastern Area; 12 p.

Schwalbe, C. P. 1981. Disparlure-baited traps for survey and detection. In: Doane, C. C.; McManus, M. L. editors. 

The gypsy moth: research toward integrated pest management. Tech. Bull. 1584. Washington, DC: U.S. 

Department of Agriculture, Forest Service; 542-548. 

Sharov, A. A., Roberts, E.A.; Ravlin, F. W.; Liebhold, A. M. 1995. Spread of gypsy moth (Lepidoptera: 

Lymantriidae) in the central Appalachians: three methods for species boundary estimation. Environmental 

Entomology 24: 1529-1538.
101

Gypsy Moth

Lymantria dispar (L.) 

Lepidoptera: Lymantriidae

Thorpe, K. W.; Ridgway, R. L. 1992. Gypsy moth (Lepidoptera: Lymantriidae) egg mass 

distribution and sampling in a residential setting. Environmental Entomology 21: 

722-730.

Objective 

To determine the distribution of L. dispar egg masses in residential settings; and to compare the cost 

effectiveness of several sampling techniques for determining their density.

Abstract: The gypsy moth, Lymantria dispar (L.), was introduced into Medford, Massachusetts in 1869, 

and is now a major defoliator of hardwoods throughout the northeastern USA and Canada. Defoliation 

results in reduced growth, decreased vigor and extensive tree mortality.

The spatial distribution of L. dispar egg masses was determined in 60 developed lots in a residential 

community of Maryland. The community was divided into low (393 egg masses per hectare) and high 

(2,656 egg masses per hectare) densities. In the high-density lots, the proportion of egg masses found on 

trees, man-made objects, and houses was 73.9, 21.6, and 4.5%, respectively. Distributions were similar in 

the low density lots. Oaks, Quercus spp., had the highest proportion of egg masses at both low and high 

population densities. The cost effectiveness of a number of potential sampling units (entire lots, back 

yards, fixed area plots, and individual trees) for determining egg mass density was also evaluated. The 

entire lot sampling units was most precise. However, 100-m2

 samples were most cost effective, and are 

therefore recommended for determining egg mass density. 

Sampling Procedure

Fixed-area plots (Kolodny-Hirsch 1986) were compared with binomial sampling procedures (Binns 

and Bostanian 1990). Residential lots were surveyed by recording the number of egg masses found 

on all objects. These data were then used in a computer simulation to compare the reliability and cost 

effectiveness of sampling the lots for egg masses using 100-m2 fixed-area plots, entire backyard plots, or 

individual trees (all species and oaks only).

Mark and delineate boundaries for each fixed-area plot sample. Locate and record the number of 

egg masses found on all trees, man-made objects, and houses. Disassemble stacked objects (such as 

firewood) to locate hidden egg masses. Use binoculars to examine taller objects, if necessary. Examine 

all egg masses in reach to determine if they are from the current (new) or previous generation (old). The 

number of egg masses (EM) per ha for each lot can be determined by:

EM/ha = EM x [new/(new + old)]/area

Note 

The authors recommend further investigations of binomial sampling techniques because of their effectiveness and the ease of which residents can aid in monitoring programs. Methods for using the fixed-ar-
102

ea plots are further discussed in Kolodny-Hirsch (1986). In low density lots, sampling time ranged from 

97 min for a fixed area plot to 219 min for entire lot samples.

References

Binns, M. R.; Bostanian, N. J. 1990. Robustness in empirically based binomial decision rules for integrated pest 

management. Journal of Economic Entomology 83: 420-427.

* 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: 122-27.
103

Gypsy Moth

Lymantria dispar (L.) 

Lepidoptera: Lymantriidae

Wallner, W. E.; Jones, C. G.; Elkinton, J. S.; Parker, B. L. 1991. Sampling low density gypsy 

moth populations. In: Gottschalk, K. W.; Twery, M. J.; Fields, S. I., editors. Proceedings 

of the U.S. Department of Agriculture Interagency Gypsy Moth Research Review--1990. 

Gen. Tech. Rep. NE-146. Radnor, PA: U.S. Department of Agriculture, Forest Service, 

Northeastern Forest Experiment Station; 40-44.

Objective 

To review the sensitivity, reliability and cost of methods for sampling low density L. dispar infestations.

Abstract: The gypsy moth, Lymantria dispar (L.), was introduced into Medford, Massachusetts in 1869, 

and is now a major defoliator of hardwoods throughout the northeastern USA and Canada. Defoliation reduces tree growth and vigor, and in combination with other stress factors can cause excessive 

tree mortality. The techniques for sampling gypsy moth populations at low densities (less than 100 egg 

masses per hectare) were compared. The use of pheromone traps has demonstrated the highest level of 

male moth detection, but cannot be related to subsequent egg mass, larval density, or defoliation, levels. 

Therefore, its use for population monitoring is limited to detection and delineation of new infestations.

A series of burlap banded trees can be used to monitor fluctuations in egg mass densities. Egg masses 

beneath bands on sample trees reflected densities on unbanded trees, and are much easier to deploy 

than other conventional sampling techniques such as fixed-radius, prism, stem, and timed walk samples. 

Populations were considered in outbreak mode when densities were greater than 100 egg masses per 

hectare, and were triggered at densities between 10-25 egg masses per hectare.

Sampling Procedure

Place a burlap band around the bole of each oak, Quercus spp., tree >7 cm diameter and 1.3 m high in 

clusters of 10 trees. Separate each cluster by 100 m to reflect an estimate that is based on 1 ha. Return 

annually to the same locations, record the number of egg masses within each band, and compute the 

mean of the 10-tree sample per plot. Populations are in outbreak mode when densities are greater than 

100 egg masses per hectare. Expect populations to reach outbreak status if densities are between 10-25 

egg masses per hectare.
104

Gypsy Moth

Lymantria dispar (L.) 

Lepidoptera: Lymantriidae

Wilson, R. W. Jr.; Fontaine, G. A. 1978. Gypsy moth egg mass sampling with fixed-andvariable-radius plots. Agric. Handb. 523. Washington, DC: U.S. Department of 

Agriculture; 46 p.

Objective 

To provide detailed procedures for sampling L. dispar egg masses.

Abstract 

The gypsy moth, Lymantria dispar (L.), was introduced into Medford, Massachusetts in 1869, and is now a 

major defoliator of hardwoods throughout the northeastern USA and Canada. Defoliation reduces tree 

growth and vigor, and in combination with other stress factors can cause excessive tree mortality. Two 

methods: sampling with fixed- and variable-radius plots (FVP) are intended for forested areas greater 

than 5 hectares in area with egg mass densities greater than 250 per hectare. Fixed-radius plots of 20 m2

are first sampled to determine the mean number of egg masses per hectare on all objects in the understory (EMx). The variable-radius plot is taken from the center of the fixed-radius plot to estimate the mean 

number of egg masses per hectare on overstory trees (EMy). The mean number of total egg masses per 

hectare (EM) is then determined by the formula EM = EMx + EMy. Topics discussed in this handbook 

include the description, organization, and execution of this survey technique, and compilation of the associated data.

Sampling Procedure

The sampling unit consists of variable-radius plots (BAF 20) for sampling overstory trees, and a fixedradius plot (20 m2

) for sampling the understory. Lay out 30 sample points systematically, with a 30-m 

spacing between plots. The authors provide considerable detail on sample size derivations, sample point 

locations, and crew organization for which we refer you to the original publication.

Fixed-radius plots: Sample fixed-radius plots first before the understory is disturbed. Locate and 

record the number of egg masses per plot. Examine all objects for egg masses except overstory trees 

(dominant, codominant, and intermediate). To determine the mean number of egg masses per acre, 

multiply the number of egg masses found by 200.

Variable-radius (prism) plots: The prism point is the center of the fixed-radius plot. If the tree is not 

alive or is receiving direct skylight on its crown, then do not include it in the sample. Record the tree 

species, d.b.h., and proceed with the egg mass census. To determine the egg mass count corrected for 

tree size, divide it by the d.b.h.2

 (inches) and then sum for each tree sampled. Then to determine the 

number of egg masses per acre, divide the sum of the weighted egg counts by the total number of trees 

sampled and multiply by 3,667.

To estimate the mean total number of egg masses per acre, add the average counts for the overstory and 

understory samples.
105

Douglas-Fir Tussock Moth

Orgyia pseudotsugata (McDunnough) 

Lepidoptera: Lymantriidae

Daterman, G. E. 1978. Monitoring and detection. In: Brooks, M. H.; Stark, R. W.; 

Campbell, R. W., editors. The Douglas-fir tussock moth: a synthesis. Tech. Bull. 

1585. Washington, DC: U.S. Department of Agriculture, Forest Service; 99-102.

Objective 

To review methods of monitoring O. pseudotsugata populations.

Abstract 

The Douglas-fir tussock moth, Orgyia pseudotsugata (McDunnough), is a major defoliator of Douglasfir, Pseudotsuga menziesii (Mirb.) Franco, and true firs, Abies spp., in western North America. Outbreaks 

occur quite unexpectedly so that a large number of trees are often defoliated before direct control 

measures can be applied. Growth loss, top-kill, and tree mortality are common during outbreaks. The 

traditional method of monitoring populations requires that early instar larvae be counted on individual 

sample trees. Such data are useful for establishing population trends, but greater resolution is required 

at low population densities to predict outbreaks. In the early 1970’s improved monitoring and sampling 

techniques for O. pseudotsugata were developed in response to devastating infestations in the Pacific 

Northwest. A review of these sampling methods is presented here.

Sampling Procedure

Most of the information contained in this chapter is presented in other reviews of O. pseudotsugata. 

Sections describe larval density estimation by beat and lower crown samples, adult monitoring by 

pheromone trapping methods and their utility for predicting population trends, and postdetection larval 

and egg surveys. This chapter on monitoring and detection is a useful supplement to other reviews if 

difficulty is encountered in understanding sampling principles and methodologies.
106

Douglas-Fir Tussock Moth

Orgyia pseudotsugata (McDunnough) 

Lepidoptera: Lymantriidae

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.

Objective 

To develop sequential sampling plans for classification of O. pseudotsugata populations.

Abstract

The Douglas-fir tussock moth, Orgyia pseudotsugata (McDunnough), periodically causes severe damage 

to Douglas-fir, Pseudotsuga menziesii (Mirb.) Franco, and true firs, Abies spp., in western North America. 

Outbreaks occur every 7-10 years and usually persist for 3-4 years. Growth loss, top-kill and tree 

mortality are common when O. pseudotsugata populations are high.

Sequential sampling plans for making quick classifications of incipient populations were developed for 

O. pseudotsugata eggs and larvae. After each tree is sampled, a sequential sampling plan is referenced, and 

sampling is discontinued when a decision threshold is reached. Populations are classified as either light or 

heavy. These plans are designed to be applied independently in a suspected infestation for distinguishing 

between low density populations and high density populations capable of reaching outbreak levels within 

one generation. 

Sampling Procedure:

Egg: Sample one, two, and one whole branches from the upper, middle and lower crown, respectively. 

Count the number of egg masses on all four branches, and express their density per 0.645 m2

. After each 

tree is sampled, reference the sequential sampling plan (Fig. 2), and continue sampling until a decision 

threshold is reached, and infestations are classified as either light or heavy.

The number of eggs in the field plan (Fig. 2) is converted to egg masses by dividing the number of eggs 

by 260. These factors can be adjusted accordingly if there is reason to believe that the average number 

of eggs per mass deviates significantly from 260.

Larvae: Cut one 43-cm foliated twig sample from the outer mid-crown, and two similar sized samples 

from the inner mid-crown. The outside crown is defined as the outer 43 cm from the tip of the main 

branch. Special care should be taken when using pole pruners so that larvae are not dislodged from 

the samples. Count the number of larvae from all three twigs, and express their density per 0.645 m2

. 

After each tree is sampled, reference the sequential sampling plan (Fig. 1), and continue sampling until a 

decision threshold is reached, and infestations are classified as either light or heavy.

Time larval samples to coincide with the presence of small larvae. If late instar larvae are sampled, 

classification may become less accurate. In endemic populations, it is much easier to locate young larvae 
107

dispersed in the foliage than egg masses. For this reason, extensive surveys for detection of low level 

populations will be obtained most efficiently by sampling larvae. 

Note: The data used in developing these sequential sampling methods were gathered from infestations 

on white fir, A. concolor (Good. and Glend.) Lindl., and may not be applicable to infestations on other 

species.. 

Figures

Figure 1. Sequential graph for classifying Douglas-fir tussock moth larvae into two population levels.
108

Figure 2. Sequential graph for classifying Douglas-fir tussock moth eggs into two population levels.
109

Douglas-Fir Tussock Moth

Orgyia pseudotsugata (McDunnough) 

Lepidoptera: Lymantriidae

Mason, R. R. 1970. Development of sampling methods for the Douglas-fir tussock 

moth, Hemerocampa pseudotsugata (Lepidoptera: Lymantriidae). Canadian 

Entomologist 102: 836-845.

Objective

To develop and compare methods of sampling O. pseudotsugata populations. 

Abstract 

The Douglas-fir tussock moth, Orgyia pseudotsugata (McDunnough), is a major defoliator of Douglas-fir, 

Pseudotsuga menziesii (Mirb.) Franco, and true firs, Abies spp., in western North America. Outbreaks occur 

quite unexpectedly so that a large number of trees are often defoliated before direct control measures 

can be applied. Growth loss, top-kill and tree mortality are common during outbreaks. Procedures that 

estimate the density of eggs and larvae of O. pseudotsugata were developed and compared with respect to 

their standard error relative to the mean.

Population density was estimated in terms of the number of eggs or larvae per 0.645 m2

 of branch area. 

A significant proportion of the variation in the density of eggs and larvae was attributed to crown level 

and outbreak status. In an outbreak, egg masses are concentrated on inside branches near the bottom 

of the crown. However, in light infestations they are often on outside branches in the upper crown. 

The mean density of larvae in the mid-crown was representative of the whole tree. Egg density was 

estimated from whole branch samples collected from three crown levels. Larval density was estimated 

from 43-cm twig samples collected from the mid-crown. Since eggs are clumped in masses and larvae 

are dispersed over the foliage, larval density was estimated with less effort. Tables are provided that list 

required sample sizes to estimate egg and larval populations with known precision.

Sampling Procedure

Eggs: Sample one branch from the upper crown, two from the mid-crown, and one from the lower 

crown. The number of eggs in each sample unit is expressed as a function of the foliated area of each 

branch. Estimate the foliated area per branch by dividing the product of length and width by two 

((W*L)/2). All insect counts are adjusted to a 0.645 m2 of branch area. The number of eggs in the 

sample is simply calculated by multiplying the average number of eggs per mass by the number of egg 

masses. Table 1 lists the number of sample trees required for four levels of precision.

Larvae: Cut one 43-cm foliated twig sample from the outer mid-crown, and two samples from the inner 

mid-crown. Special care should be taken when using pole pruners so that larvae are not dislodged from 

the samples. The number of larvae on each sample unit is expressed as a function of the foliated area of 

each twig. Calculate twig area as in the egg sample. Table 2 lists the number of sample trees required for 

four levels of precision.
110

The authors suggest that a standard error within 20% of the mean is adequate for estimating population 

densities of O. pseudotsugata. This level of error can be attained by sampling 11 trees for larval 

populations in heavy infestations. In light larval or egg populations more samples would be required.

Notes: In practice, surveys of light populations are better handled through different sampling techniques 

involving sequential analysis. The authors suggest that this data should not be extrapolated to include 

trees greater than 12 m in height. Because larvae are better dispersed through the foliage than eggs, the 

variance is significantly smaller for larvae than for eggs. Sampling the larval population will yield more 

accurate results, with much less sampling effort, than sampling eggs.

Tables

Table 1. Number of sample trees required at different levels of error for estimating egg populations at a 

sample point.

Mean no. of eggs 

per 0.645 m2

 (1,000 m2)

Standard error as per cent of mean

5 10 20 40

2 1,141 285 71 18

5 1,064 266 66 16

10 981 245 61 15

20 961 240 60 15

30 955 239 60 15

40 951 238 60 15

50 949 237 59 15

60+ 948 237 59 15

Table 2. Number of sample trees required at different levels of error for estimating larval 

populations at a sample point.

Mean no. of larvae per

0.645 m2

 (1,000 in2)

Standard error as per cent of mean

5 10 20 40

2 370 92 23 6

5 260 65 16 4

10 210 52 13 3

20 190 48 12 3

30 183 46 12 3

40 180 45 11 3

50 178 44 11 3

60+ 176 44 11 3

Tables reprinted with permission of the Canadian Entomologist, January 15, 2001.
111

Douglas-Fir Tussock Moth

Orgyia pseudotsugata (McDunnough) 

Lepidoptera: Lymantriidae

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.

Objective 

To develop a practical sampling plan for estimating very low densities of O. pseudotsugata.

Abstract 

The Douglas-fir tussock moth, Orgyia pseudotsugata (McDunnough), is a major defoliator of Douglas-fir, 

Pseudotsuga menziesii (Mirb.) Franco, and true firs, Abies spp., in western North America. Outbreaks occur 

quite unexpectedly so that a large number of trees are often defoliated before direct control measures can 

be applied. Growth loss, top-kill and tree mortality are common during outbreaks. Conventional control 

methods of sampling larvae include examining and measuring branches removed from the mid-crown 

with a pole pruner (Mason 1969, 1970).

A new method is described for estimating larval density rapidly when populations are very low. This 

procedure is a large improvement over existing methods because observations are quickly and efficiently 

made in the lower crown without destructive sampling. Three branches are sampled using a beat cloth, 

and the presence or absence of larvae is recorded for each tree. Data are collected on the proportion of 

trees that contain larvae, which can be used to estimate the density in the lower and mid-crown.

Sampling Procedure: Select three branches randomly from the lower crown of Douglas-fir and beat 

over a portable drop cloth (61 by 123 cm), recording the presence or absence of larvae in each sample. 

The beat cloth should be placed within 56 cm of the branches. If a larva is found on the first branch, 

it is unnecessary to sample the remaining branches. The density of larvae in the lower crown can be 

estimated by substituting 2.0 for R in the equation:

 L = -2Rln(1 - Px

)

where, Px

 is the estimated proportion of sample units in the lower crown containing larvae. Older larvae 

migrate toward the lower crown prior to pupation, and therefore R decreases through the season and 

affects the estimate of larval density. An R of 2.0 is recommended for sampling first and second instars, 

1.5 for third and fourth instars, and 1.0 for fifth and sixth instars and pupae. Table 1 provides mid-crown 

larval density estimates for these three R values. 

Notes 

The sampling plan presented is for low density, sub-outbreak populations and should be applied to first 

and second instar larvae. The density equation assumes that the same frequency distribution of larvae in 

the lower crown applies to that of the mid-crown where data were collected originally. The distribution 
112

of larvae within each crown level, regardless of density, follows the same distribution. 

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.

Table

Table 1. Conversion of the proportion of infested lower crown samples (Px) to density of 

larvae in the mid-crown (M ). Densities are calculated from (M ) = -2R ln(1 - px) for three values 

of R1 and expressed as number of larvae per 0.645 m2

.

M M

Px R = 1.0 R = 1.5 R = 2.0 Px R = 1.0 R = 1.0 R = 1.0

.001 .002 .003 .004 .31 .74 1.11 1.48

.002 .004 .006 .008 .32 .77 1.16 1.54

.003 .006 .009 .012 .33 .80 1.20 1.60

.004 .008 .012 .016 .34 .83 1.25 1.66

.005 .010 .015 .020 .35 .86 1.29 1.72

.006 .012 .018 .024 .36 .89 1.34 1.78

.007 .014 .021 .028 .37 .92 1.39 1.85

.008 .016 .024 .032 .38 .96 1.43 1.91

.009 .018 .027 .036 .39 .99 1.48 1.98

.01 .02 .03 .04 .40 1.02 1.53 2.04

.02 .04 .06 .08 .41 1.06 1.58 2.11

.03 .06 .09 .12 .42 1.09 1.63 2.18

.04 .08 .12 .16 .43 1.12 1.68 2.25

.05 .10 .15 .20 .44 1.16 1.74 2.32

.06 .12 .19 .25 .45 1.20 1.79 2.39

.07 .15 .22 .29 .46 1.23 1.85 2.46

.08 .17 .25 .33 .47 1.27 1.90 2.54

.09 .19 .28 .38 .48 1.31 1.96 2.62

.10 .21 .32 .42 .49 1.35 2.02 2.69

.11 .23 .35 .47 .50 1.39 2.08 2.77

.12 .26 .38 .51 .51 1.43 2.14 2.85
113

.13 .28 .42 .56 .52 1.47 2.20 2.94

.14 .30 .45 .60 .53 1.51 2.27 3.02

.15 .32 .49 .65 .54 1.55 2.33 3.11

.16 .35 .52 .70 .55 1.60 2.40 3.19

.17 .37 .56 .74 .56 1.64 2.46 3.28

.18 .40 .60 .79 .57 1.69 2.53 3.38

.19 .42 .63 .84 .58 1.74 2.60 3.47

.20 .45 .67 .89 .59 1.78 2.67 3.57

.21 .47 .71 .94 .60 1.83 2.75 3.67

.22 .50 .75 .99 .61 1.88 2.82 3.77

.23 .52 .78 1.04 .62 1.94 2.90 3.87

.24 .55 .82 1.10 .63 1.99 2.98 3.98

.25 .58 .86 1.15 .64 2.04 3.06 4.09

.26 .60 .90 1.20 .65 2.10 3.15 4.20

.27 .63 .94 1.26 .66 2.16 3.24 4.32

.28 .66 .99 1.31 .67 2.22 3.33 4.43

.29 .68 .102 1.37 .68 2.28 3.42 4.56

.30 .71 .107 1.43 .69 2.34 3.51 4.68

.70 2.41 3.61 4.82

1

R = 2.0 is recommended for sampling first and second instars (small larvae), R = 1.5 for third and fourth 

instars (medium larvae), and R = 1.0 for fifth and sixth instars (large larvae).
114

Douglas-Fir Tussock Moth

Orgyia pseudotsugata (McDunnough) 

Lepidoptera: Lymantriidae

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.

Objective 

To develop a sequential sampling plan for classifying O. pseudotsugata infestations based on the occurrence 

of first instar larvae in the lower crown.

Abstract 

The Douglas-fir tussock moth, Orgyia pseudotsugata (McDunnough), is a major defoliator of Douglas-fir, 

Pseudotsuga menziesii (Mirb.) Franco, and true firs, Abies spp., in western North America. Outbreaks occur 

quite unexpectedly so that large numbers of trees are often defoliated before direct control measures can 

be applied. Growth loss, top-kill and tree mortality are common during outbreaks. The early recognition 

of impending outbreak conditions is essential for managing this insect.

A sequential sampling plan was described for identifying the outbreak potential of O. pseudotsugata

populations. The plan uses a technique for sampling early instar larvae by non-destructive examination 

of lower crown foliage (Mason 1977). After each tree is sampled, the sequential sampling plan is 

referenced (Figs. 2, 4), and sampling is continued until a decision is reached. Infestations are classified as 

either low level or suboutbreak, which indicates a population capable of reaching outbreak levels within 

one generation. The sampling plan is applied independently on individual plots to classify the density of 

each plot. It is an appropriate method for screening populations quickly in evaluation surveys, but is not 

intended as a single evaluation of large forested areas.

Sampling Procedure 

Trees are sampled randomly within each 2 ha plot. Sampling techniques are described in detail by Mason 

(1977). Beat three branches selected randomly from the lower crown of Douglas-fir against a portable 

drop cloth, and record the presence or absence of larvae in each sample. If a larva is found on the first 

branch, it is unnecessary to sample the remaining branches. After each tree is sampled, reference the 

sequential sampling plan (Fig. 2), and continue sampling until a decision threshold is met (i.e., infestations 

are classified as either low level or capable of outbreak levels within one year). If no decision is reached 

after sampling 20 trees, the infestation is classified as intermediate.

Notes Sampling must be conducted after egg hatch when the majority of larvae are first instar, and 

new shoot growth is at least 2.5-5 cm in length.
115

Reference

* 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.

Figure
116

Douglas-Fir Tussock Moth

Orgyia pseudotsugata (McDunnough) 

Lepidoptera: Lymantriidae

Mason, R. R. 1979. How to sample larvae of the Douglas-fir tussock moth. Agric. Handb. 

547. Washington, DC: U.S. Department of Agriculture, Forest Service; 15 p.

Objective 

To provide a system that classifies O. pseudotsugata populations into general density categories that are 

meaningful for evaluating outbreak potential. 

Abstract 

The Douglas-fir tussock moth, Orgyia pseudotsugata (McDunnough), is a major defoliator of Douglas-fir, 

Pseudotsuga menziesii (Mirb.) Franco, and true firs, Abies spp., in western North America. Outbreaks occur 

quite unexpectedly so that a large number of trees are often defoliated before direct control measures 

can be applied. Growth loss, top-kill and tree mortality are common during outbreaks. The early 

detection of impending outbreak conditions is essential for managing this insect, and is measured by the 

number of larvae present when populations are low. This review contains detailed information on how to 

properly sample O. pseudotsugata populations.

Sampling Procedure 

Most of the information contained in this handbook is presented elsewhere. However, the author 

provides clear and concise instructions explaining how to estimate larval densities by sampling the midcrown (Mason 1969, 1970) and lower crown (Mason 1977) of host trees. Sequential sampling plans 

are also reviewed (Mason 1969, 1978). This manual is a useful supplement to previous publications, 

particularly if difficulty is encountered in understanding sampling methods.

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; 11p.

* 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. 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.
117

Douglas-Fir Tussock Moth

Orgyia pseudotsugata (McDunnough) 

Lepidoptera: Lymantriidae

Mason, R. R. 1987. Frequency sampling to predict densities in sparse populations of the 

Douglas-fir tussock moth. Forest Science 33: 145-156.

Objectives 

To derive and compare two models for estimating density from p; and to determine which is most 

versatile over a wide range of larval ages and densities.

Abstract 

The Douglas-fir tussock moth, Orgyia pseudotsugata (McDunnough), is a major defoliator of Douglas-fir, 

Pseudotsuga menziesii (Mirb.) Franco, and true firs, Abies spp., in western North America. Outbreaks occur 

quite unexpectedly so that a large number of trees are often defoliated before direct control measures 

can be applied. Growth loss, top-kill and tree mortality are common during outbreaks. The density of 

insects in the mid-crown is the standard expression of abundance for analysis and management of O. 

pseudotsugata populations.

Procedures (Mason 1977, 1978, 1979) have been developed for predicting mid-crown densities of 

first and second instar larvae by sampling the lower crown where foliage is examined easily without 

destructive sampling methods. In that scheme, a value p (proportion of samples containing at least one 

insect) was estimated from examination of lower crown branches, and translated into mid-crown density 

by a correction factor for the vertical distribution of larvae within crowns (Mason 1977). Mid-crown 

densities calculated from mean proportions from the lower crown (M = -17.754d-0.598 ln (1 – p)) and 

densities from direct mid-crown sampling compared favorably. A chi-square goodness-of-fit test between 

calculated and observed values indicated density estimates from the two sampling methods did not differ 

significantly (P < 0.005). The model based on lower crown samples is therefore recommended for use 

as density estimations are easier to obtain than direct sampling of the mid-crown. It is also applicable to 

any sample of which average insect age is known or can be approximated.

Sampling Procedure

Take three lower crown samples from 12-15 trees in 10 1-ha plots. Accessible limbs are sampled by 

beating the distal 45-cm of each branch over a drop cloth to dislodge insects. Determine the number 

of sample units with one or more tussock moths present and the proportion (p) of infested trees. The 

theoretical model is then used to calculate the mean number of insects per square meter of branch area 

to derive a mid-crown population estimate:

M = -17.754d-0.598 ln (1 – p),

where d is the average age in days since egg hatch (if predominant instar is between classes use midpoint):
118

Instar I 5

Instar II 15

Instar III 25

Instar IV 35

InstarV 45

Instar VI 55

Pupae 60

Note

The models were developed for low density populations of O. pseudotsugata.

References

* 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. 1979. How to sample larvae of the Douglas-fir tussock moth. Agric. Handb. 547. Washington, DC: 

U.S. Department of Agriculture, Forest Service; 15 p.
119

Douglas-Fir Tussock Moth

Orgyia pseudotsugata (McDunnough) 

Lepidoptera: Lymantriidae

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.

Objectives 

To develop a method of assessing egg mass density rapidly within non-defoliated stands; and to predict if 

significant damage will occur the following year.

Abstract

The Douglas-fir tussock moth, Orgyia pseudotsugata (McDunnough), is a major defoliator of Douglas-fir, 

Pseudotsuga menziesii (Mirb.) Franco, and true firs, Abies spp., in western North America. Defoliation can 

often be severe and cause tree mortality during the first year of defoliation. Outbreaks erupt suddenly 

and often synchronously in patches over large forested areas. To reduce losses, early warning of potential 

outbreaks are necessary to schedule control operations.

A sequential egg mass sampling system, based on visual scanning of the lower branches of Douglasfir, was designed. No consistent trend in egg mass density per branch could be found among crown 

levels, and no level proved superior as a representative of the whole tree. Therefore, the lower whorl 

of branches was selected for survey purposes because of sampling efficiency. The sampling system is 

designed to assess egg mass density rapidly within non-defoliated stands and predict defoliation levels for 

the following year.

The number of egg masses on three lower branches on each of 20 trees is examined, and a sequential 

graph is referenced (Fig. 2). As samples are taken, the cumulative number of egg masses is plotted over 

the number of trees sampled. Sampling continues until a decision is met and defoliation is predicted as 

none or little (0-0.7), noticeable (0.7-2.0), or severe (≥2.0 egg masses per three branch sample).

Sampling Procedure

Autumn egg mass surveys provide the basis for a rough estimate of defoliation the following year since 

there is considerable loss of egg masses during winter and early spring. However, this plan can be used as 

a tool to determine the potential of an outbreak. Its main advantage is the efficiency at which egg mass 

densities can be estimated, and the amount of advanced notice available for scheduling control programs 

over conventional sampling techniques.

Determine if egg masses are present, and then make a circuit to locate the center and extent of the 

infestation. Count the number of egg masses on three lower branches on each of 20 trees. Reference 

the sequential graph to determine the upper and lower stop sampling lines, representing the number of 

samples required to determine the density within 20% of the true mean 95% of the time (Fig. 2). As 

samples are taken, the cumulative number of egg masses is plotted over the number of trees sampled. At 
120

the point of crossing an upper or lower stop sampling line, a population estimate can be made. If the 

total number of egg masses found is 4 or 40, stop sampling and calculate the average number of egg 

masses per tree. If the total number of egg masses is 5-39, sampling continues until a decision is met 

and defoliation is predicted as either none or little, noticeable, or severe.

Notes 

This system is designed as an early detection tool to predict potential outbreaks in non-defoliated stands 

with branches low enough for visual observation of egg masses. Please refer to our review of Shepherd 

and others (1985) for more detailed information on defoliation classes.

Reference

* Shepherd, R. F.; Otvos, I. S.; Chorney, R. J. 1985. Sequential sampling for Douglas-fir tussock moth egg masses in 

British Columbia. Joint Rep. 15. Canadian Forest Service, Pacific Forest Research Centre. 7 p. 

Figure

Figure 2 reprinted with permission of the Canadian Entomologist, January 15, 2001.
121

Douglas-Fir Tussock Moth

Orgyia pseudotsugata (McDunnough) 

Lepidoptera: Lymantriidae

Shepherd, R. F. 1985. Pest management of Douglas-fir tussock moth: estimating larval 

density by sequential sampling Canadian Entomologist 117: 1111-1115. 

Objective

To determine densities of early instar larvae of O. pseudotsugata with known precision.

Abstract

The Douglas-fir tussock moth, Orgyia pseudotsugata (McDunnough), is a major defoliator of Douglas-fir, 

Pseudotsuga menziesii (Mirb.) Franco, and true firs, Abies spp., in western North America. Defoliation can 

often be severe and cause tree mortality during the first year of defoliation. A sequential sampling system 

for estimating early instar larval density within 20% of the mean 95% of the time is presented. The 

system was used to measure lower crown densities equivalent to 4.3-130 larvae per square meter at midcrown as part of an assessment system for control decision-making.

The number of larvae per three branch sample was determined and compared with the sequential 

sampling plan. Sampling was continued until a decision was met (Table 1). Above the upper stopsampling line, noticeable defoliation is expected. Below the lower stop-sampling line, the larval density 

is at least one generation away from causing noticeable defoliation. A minimum of 10 trees was sampled 

before a decision was met.

Sampling Procedure

Incipient outbreaks are initially detected by sampling low level larval densities (Mason 1977) or by 

pheromone trapping and scouting for egg masses (Shepherd and others 1984). Once an infestation is 

located, select three branches randomly from each of 10 trees per plot, and beat the terminal portions 

over a 60 by 90-cm canvas trap to dislodge larvae. Record the number of larvae per sample. Sampling 

should coincide with an abundance of first and second instar larvae, and before new foliage has begun 

to change color. After 30 samples (10 trees), reference the sequential sampling plan (Table 1), and 

continue sampling until a decision is made. Larval populations will be classified as capable of producing 

noticeable defoliation or at least one generation away from causing noticeable defoliation. 

Note

This system is designed to be used in stands that were not defoliated previously. The relationship 

between lower and mid-crown densities is discussed.

References

* 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.
122

* 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.

Table

Table 1. Densities of Douglas-fir tussock moth larvae where sampling is discontinued. A density below 

the lower stop-sampling line indicates that the larval density is at least 1 generation away from causing 

noticeable defoliation. 

Stop sampling when cumulative number of larvae is

equal to or

No. of trees Less than More than

10 17 68

12 23 64

14 29 61

16 35 59

18 41 58

20 47 57

22 53 56

24 59 55

Table 1 reprinted with permission from the Canadian Entomologist, January 15, 2001.
123

Douglas-Fir Tussock Moth

Orgyia pseudotsugata (McDunnough) 

Lepidoptera: Lymantriidae

Shepherd, R. F.; Gray, T. G.; Chorney, R. J.; Daterman, G. E. 1985. Pest management 

of Douglas-fir tussock moth, Orgyia pseudotsugata (Lepidoptera: Lymantriidae): 

monitoring endemic populations with pheromone traps to detect incipient 

outbreaks. Canadian Entomologist 117: 839-847.

Objective

To develop a trap-based monitoring system that follows population trends of O. pseudotsugata adults 

through endemic levels and predicts incipient outbreaks.

Abstract 

 

The Douglas-fir tussock moth, Orgyia pseudotsugata (McDunnough), is a major defoliator of Douglas-fir, 

Pseudotsuga menziesii (Mirb.) Franco, and true firs, Abies spp., in western North America. Defoliation can 

often be severe and cause tree mortality during the first year of defoliation. 

The number of O. pseudotsugata adults caught in delta-shaped sticky traps baited with pheromone lures 

was compared with egg mass densities and subsequent defoliation. A lure containing 0.01% pheromone 

by weight was more effective at predicting population trends than baits having concentrations of 0.0001, 

0.001, 0.1, or 1.0%. Trap saturation occurred at 40 moths per trap. To achieve a standard error of 30% 

of the mean, six traps were required at each site. A threshold density of 25 moths per trap provided a 

warning of potential outbreaks, causing defoliation up to 12-km from the trap site.

Sampling Procedure 

 

Construct delta-shaped traps (695 cm2) from 2 L orange, paper milk cartons with the interior coated 

with TanglefootTM (Tanglefoot Co., Grand Rapids, MI). Place six traps, with one 3 by 5-mm PVC lure 

containing 0.01% of O. pseudotsugata pheromone (Z-6-heneicosen-11-one) impaled to the roof interior, at 

each monitoring site. Hang traps >30 m apart on live host branches 2-2.5 m above ground.

A trap density threshold of 25 moths per trap is used to indicate that the population is about 2 years 

from outbreak, and should be followed up by more detailed egg mass surveys. Space survey plots 1-5 

km apart the summer before defoliation is predicted to occur. The authors recommend a pre-outbreak 

warning system consisting of a continuous pheromone-trap monitoring system to follow population 

trends, and a sequential egg mass survey to identify concentrated areas of outbreak where defoliation can 

be expected (Shepherd and others 1984).

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.
124

Douglas-Fir Tussock Moth

Orgyia pseudotsugata (McDunnough) 

Lepidoptera: Lymantriidae

Shepherd, R. F.; Otvos, I. S.; Chorney, R. J. 1985. Sequential sampling for Douglas-fir 

tussock moth egg masses in British Columbia. Joint Rep. 15. Canadian Forest 

Service, Pacific Forest Research Centre. 7 p. 

Objective

To develop a sequential sampling plan designed to predict defoliation levels the following summer based 

on fall egg mass densities. 

Abstract

The Douglas-fir tussock moth, Orgyia pseudotsugata (McDunnough), is a major defoliator of Douglas-fir, 

Pseudotsuga menziesii (Mirb.) Franco, and true firs, Abies spp., in western North America. Defoliation can 

often be severe and cause tree mortality during the first year of defoliation. This field guide describes an 

easy-to-use survey for determining egg mass density and predicting defoliation levels.

The use of pheromone traps will provide a warning when O. pseudotsugata populations are approaching 

outbreak levels. However, pheromone traps do not pinpoint the exact location of an outbreak, or 

the level of expected defoliation. The latter information can be determined by a sequential egg mass 

sampling program (Shepherd and others 1984). This program is designed to determine the average 

number of egg masses per tree within 20% of the true mean 95% of the time. Defoliation predictions 

are based on mean egg mass totals per three lower crown branches, and are classified as either light, 

moderate, or severe.

Sampling Procedure

In the fall, after pheromone traps have indicated that O. pseudotsugata egg masses may be present, examine 

all susceptible stands with little or no defoliation in the general vicinity of the pheromone traps. Walk 

through each stand, looking on the lower side of branches for egg masses. Be careful not to confuse old 

egg masses or cocoons for new, viable egg masses. If egg masses are found, search for the area where 

egg mass density appears to be highest and mark the center of the infestation.

Select 20 Douglas-fir trees randomly around the center of the infestation with at least three full-sized 

lower branches close enough to the ground so that new egg masses can be seen easily. Record the 

number of egg masses on the three lower branches. Calculate the average number of egg masses per tree 

and determine the predicted defoliation class:

0-0.7 egg masses per three lower branches per tree: No or light defoliation as characterized by less than half of 

the trees suffering complete defoliation of current foliage in the upper crown and only minor damage to 

old foliage.

0.7-2.0 egg masses per tree: Noticeable defoliation characterized by most current foliage and almost half 

of the older foliage being damaged. Usually significant growth loss occurs, but only minor dieback and 
125

mortality is observed. 

0.7-2.0 egg masses per tree: Severe defoliation as characterized by most current foliage and more than half 

of the older foliage consumed. At least 20% of trees will be completely defoliated, and significant 

growth loss, dieback, and mortality will occur.

Note

It is the point of highest egg mass density within the stand that is surveyed so the resulting defoliation 

predictions are worst case scenarios and may not reflect stand means.

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.
126

Saddled Prominent

Heterocampa guttivitta (Walker) 

Lepidoptera: Notodontidae

Grimble, D. G.; Kasile, J. D. 1974. A sequential sampling plan for saddled prominent 

eggs. Applied Forestry Research Inst. Rep. 15. Syracuse, NY: State University of 

New York, College of Environmental Science and Forestry; 15 p.

Objective

To develop a sequential sampling plan for H. guttivitta eggs to estimate defoliation levels in sugar maple 

stands.

Abstract

The saddled prominent, Heterocampa guttivitta (Walker), is a native defoliator of hardwood forests. 

Preferred hosts include sugar maple, Acer saccharum Marsh., and American beech, Fagus grandifolia Ehrh., 

but nearly all deciduous trees are attacked during outbreaks. This insect is also an important pest of 

sugarbushes in the northeastern USA and Canada. A sequential sampling plan was presented for 

evaluating the risk of defoliation in sugar maple stands.

The sample unit consisted of 10 individual leaf-clusters (about 40 leaves) removed by pole-pruners from 

a branch tip. By examining a series of foliage samples, field workers can predict the threat of defoliation 

as either negligible (<40%) or severe (>70%) based on the cumulative number of viable eggs and newlyhatched larvae. A minimum of nine samples is required, and a maximum of 30 are examined before 

sampling is discontinued, and defoliation is classified as undetermined until another sample can be 

conducted (i.e., 2-3 days later).

Sampling Procedure

Locate one plot per 10-14 ha in stands where sugar maple is the dominant species. Remove 10 individual 

leaf-clusters (about 40 leaves) randomly from a branch tip 61-cm in length located as high in the crown 

of sugar maple as can be reached with pole pruners. A leaf cluster is defined as that group of maple 

leaves (2-4) that develop from a single bud. Time sampling to occur at about 10% egg hatch (mid-June 

through early July).

Once the foliage samples are removed, record the total number of eggs (parasitized and viable) and 

larvae found. Eggs, which are light green in color when fresh, darkening with age, are deposited singly 

on the underside of leaves. Take a minimum of nine branch samples, and calculate the cumulative 

number of H. guttivitta eggs and larvae. Reference the sequential sampling plan (Fig. 6), and continue 

sampling until a decision is met and defoliation is classified as light or severe. After a maximum of 

30 samples, discontinue sampling and classify defoliation levels as undetermined. Visit each sampling 

location at least once during the late-larval feeding period to verify defoliation levels.

Notes

The distribution of H. guttivitta eggs follows a Poisson distribution. The errors of misclassifying defoliation were set at 0.10 (light) and 0.05 (severe). Investigators should be familiar with the lifecycle of H. 
127

guttivitta and be able to distinguish its eggs from those of other forest Lepidoptera.

Figure

Figure 6 reprinted with permission from SUNY-Syracuse, January, 15, 2001.
128

Orangestriped Oakworm

Anisota senatoria (J. E. Smith) 

Lepidoptera: Saturniidae

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.

Objective

To determine if a monitoring program coupled with the establishment of an aesthetic injury level (AIL) 

could be used to manage A. senatoria with minimal insecticide inputs.

Abstract

The orangestriped oakworm, Anisota senatoria (J.E. Smith), is a native defoliator of various oaks, Quercus

spp., in the eastern USA and Canada. In the 1980’s, this species became a major pest of urban oak 

plantings along city streets in Norfolk, Virginia. Insecticide sprays were applied by city employees at 

the request of citizens to control this pest. In >50% of the citizen requests, trees had <5% defoliation. 

Justification for an urban pest management program for A. senatoria was based on the economic value of 

urban oak trees ($5,131 per tree), and the large insecticide volumes sprayed for control.

The authors established an AIL based on a citizens survey to measure defoliation levels that were 

acceptable to homeowners. Additionally, the effects of different levels of defoliation on tree vigor were 

measured by root starch content. Based on the citizens survey and root starch analyses, a 25% AIL 

threshold was used to determine if insecticide applications were required. Monitoring and establishment 

of the AIL resulted in a decrease in pesticide usage by 80% at a cost savings of 55%.

Sampling Procedure

Monitor trees during peak larval periods (mid-August through September), and estimate visually the 

defoliation by dividing the tree into four quadrants and summing the estimated percent defoliation in 

each quadrant. Apply insecticides only when trees have >25% defoliation at the time of monitoring. 

This simple monitoring technique results in an 80.3% decrease in pesticide usage, and a 55% cost 

reduction in materials and labor.

Note

The results are applicable to high-value oak stands.
129

Orangestriped Oakworm

Anisota senatoria (J. E. Smith) 

Lepidoptera: Saturniidae

Coffelt, M. A.; Schultz, P. B. 1994. Within-tree distribution and a fixed-precision level 

sampling plan for the orangestriped oakworm (Lepidoptera: Saturniidae). Journal 

of Economic Entomology 87: 382-388

Objective

To determine the minimum number of samples required to estimate within-tree population density of 

eggs and early instar larvae of A. senatoria with known sampling error.

Abstract

The orangestriped oakworm, Anisota senatoria (J. E. Smith), is a native defoliator of various oaks, Quercus

spp., in the eastern USA and Canada. Outbreaks have recently become severe in some urban areas of 

Virginia, leading to the development of integrated pest management strategies (Coffelt and Schultz 1990). 

The within-tree distribution of A. senatoria was studied to develop a fixed-precision-level sampling plan 

for eggs and early instar larvae that determines the minimum number of branchlet samples to estimate 

within-tree density on pin oak, Quercus palustris Muench. (Fig. 1).

Sampling Procedure

Sample 30 cm of a branch tips for eggs and first and second instar larvae during the last two weeks of 

July. Sampling should be conducted in all cardinal directions beginning at the drip line and working 

inward. Estimate the number of eggs per egg mass to the nearest 25 by determining the area covered by 

an average-sized pin oak leaf and establishing a visual comparison on an area basis.

The number of samples necessary to estimate the population mean with known sampling error can be 

determined using the model presented in Fig. 1. Error levels of 20, 25, and 30% are given. The number 

of within-tree samples needed for these levels of precision at various egg and early instar densities can be 

calculated using the following equation:

log n = (log a - 2 log D0

) - (2 - b) log x

where, a is the slope value, b is the intercept, n is the required number of samples, and D0

 is the fixed level 

of error in terms of the SE/mean.

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.
130

Figure

Figure 1 reprinted with permission from the Journal of Economic Entomology, January 

15, 2001.
131

Pandora Moth

Coloradia pandora Blake 

Lepidoptera: Saturniidae

Schmid, J. M.; Bennett, D.; Young, R. W.; Mata, S.; Andrews, M.; Mitchell, J. 1982. 

Sampling larval populations of the pandora moth. Res. Note RM-421. Fort 

Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Research Station; 5 p.

Objective

To develop an effective and efficient sampling method for determining larval densities of C. pandora.

Abstract

The pandora moth, Coloradia pandora Blake, is a pest of western pines, particularly ponderosa, Pinus 

ponderosa Dougl. ex Laws, lodgepole, P. contorta Dougl. ex Loud., and Jeffrey pine, P. jeffreyi Grev. and Balf. 

Coloradia pandora generally requires two years to complete its life cycle, but some individuals may take up 

to 6 years. Different combinations of trees per plot, branch samples per tree, and cardinal direction per 

sample were evaluated for estimating larval populations on ponderosa pine. The mean number of larvae 

per branch did not differ among sampling schemes. Aspect had a significant effect on larval density. 

Each plot consisted of sampling one branch from one tree. When larval counts averaged 2-4 larvae per 

branch, 50 trees must be sampled to achieve an estimate within 20% of the true mean (P = 0.05). The 

results are discussed in relation to operational sampling procedures for determining larval density with 

known precision. 

Sampling Procedure

Collect one branch from each tree. Remove branch tips 40-60 cm long from 8-10 m aboveground with a 

pole-pruner. Include only one sample from the north aspect in every four samples. Branch tips should 

be well-foliated and consist of a distinct main stem with several lateral shoots (i.e., without excessive 

branching). Count and record the number of larvae. 

When larval counts average 2-4 larvae per branch, sample 50 trees to achieve an estimate within 20% 

of the true mean (P = 0.05). If an estimate within 10% of the mean is required, then sample 200 trees. 

When larval counts average less than 1.5 per branch, more than 100 trees will need to be sampled to 

estimate larval densities within 20% of the true mean.
132

Two-Year Cycle Spruce Budworm

Choristoneura biennis Freeman 

Lepidoptera: Tortricidae

Harris, J. W. E. 1963. Sampling the egg stages of the two-year-cycle spruce budworm 

near Babine Lake, British Columbia. Forestry Chronicle 39: 199-204.

Objective

To determine the most representative location to estimate egg mass densities of the 2-year cycle spruce 

budworm, C. biennis.

Abstract

The 2-year cycle spruce budworm, Choristoneura biennis Freeman, occurs exclusively in high elevation 

stands of alpine 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, where it overwinters the first 

year. The second year, the fourth instars resume feeding, pupate, become adults, lay eggs, and their 

progeny develop until the second instar, where they overwinter to complete the 2-year life cycle. The 

last three larval instars of C. biennis (i.e., fourth, fifth, and sixth) cause most of the defoliation. Periodic 

outbreaks occur every 30 years and can last 5-10 years. A study was carried out 64-km east of Smithers, 

British Columbia, Canada, to determine the best location(s) to sample egg masses. All sites were located 

>900 m in elevation within alpine fir-white spruce stands. Egg mass densities were estimated for locality, 

tree species, crown side, crown level, stand level, and branch size.

When C. biennis densities were high, tree species, locality, and aspect did not explain a significant 

proportion of the variation in egg mass densities. A significant proportion of the variation in egg mass 

densities, however, was explained by sample tree, crown level, stand level, and branch size (whole branch 

or 45-cm tip) in the lower crown. An acceptable estimate of egg densities was obtained by sampling 

one 45-cm branch tip from the mid-crown portion of as many overstory trees as possible. When C. 

biennis densities were low, locality explained a significant proportion of the variation in egg mass density. 

Therefore, more localities need to be sampled to obtain representative estimates of C. biennis egg mass 

densities when budworm densities are at low population levels.

Sampling Procedure

Select as many sample trees as feasible from a representative area of the stand. For example, the authors 

suggest that 230 sample trees would give a sampling error of 10%. Sample trees should be limited to 

overstory alpine fir or white spruce. Cut a 45-cm branch tip from the mid-crown portion of each sample 

tree, and calculate the foliated area by multiplying the length of the branch by one-half of its width. 

Count the number of egg masses on each sample and divide by the area to obtain an estimate of egg 

density.

Note

Lower crown sampling is acceptable if for some reason it is impossible to sample the mid-crown.
133

Spruce Budworm

Choristoneura fumiferana (Clemens) 

Lepidoptera: Tortricidae

Allen, D. C.; Dorais, L.; Kettela, E. G. 1984. Survey and detection. In: Schmitt, D. M.; 

Grimble, D. G.; Searcy, J. L. editors. Spruce budworm handbook: managing the 

spruce budworm in eastern North America. Agric. Handb. 620. Washington, DC: 

U.S. Department of Agriculture, Forest Service; 21-36.

Objective

To provide a summary of survey and detection, defoliation, impact assessment, and hazard rating 

methods for C. fumiferana populations.

Abstract: The 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 can last 5-10 years. The Canada-United States Spruce Budworms Program (CANUSA) 

published a compilation of current research findings related to the spruce budworm. Survey and 

detection methods, as well as defoliation estimation, for endemic and epidemic populations are presented.

Sampling Procedure

Sparse populations: Light traps and pheromone traps are commonly used to survey low-density 

populations and to detect if an outbreak is imminent. Black light traps are often used to obtain a relative 

estimate of budworm abundance and population trends in specific areas over time. This technique can 

be used to detect increasing populations well before defoliation becomes evident. To be effective, traps 

should be placed in areas having the highest budworm hazard.

Traps baited with Fulure (95:5 blend of (E)- and (Z)-11-tetradecendal) are very effective attractants 

when used in sparse (i.e., less than 1 larva per 45-cm branch tip) populations. Place traps, equipped with 

a killing agent, in a single five-trap cluster 40 m between traps (Table 3.3) in stands of highest hazard. 

Suspend the pheromone lure below the cover of the trap.

Outbreak populations: Egg masses: Egg mass surveys are used most commonly to determine budworm 

population levels. Intensive surveys use one plot for every 1,000 to 12,000 ha whereas extensive ones use 

one plot every 15,000 ha or 40 km of road. The size of the sample unit as well as the number of samples 

collected varies by province and state. Egg mass density is expressed differently as well (see Tables 3.1 

and 3.2 in the original publication). Refer to the original publication for more detail.

Overwintering second instar survey: This method is used most commonly to check results of egg mass 

surveys, adjust infestation forecasts, and identify stands that are candidates for control. The techniques 

used are forced emergence rearing and a sodium hydroxide (NaOH) wash. This technique has been 

described in detail in Sanders (1980). The method of expressing second instar survey data varies by 

province and state (Table 3.3).
134

Large larvae: Size as well as number of samples collected varies by province and state. Please refer to 

original publication and Sanders (1980) for details.

Defoliation assessment: Please refer to Sanders (1980) for details concerning the sampling procedure.

Note 

Our review of Sanders (1980) in this publication covers the same techniques mentioned here. Please also 

refer to the original publication for more information.

Reference

* Sanders, C. J. 1980. A summary of current techniques used for sampling spruce budworm populations and 

estimating defoliation in eastern Canada. Rep. O-X-306. Canadian Forest Service, Great Lakes Forest 

Research Center; 34 p.

Table

Table 3.3 Relationship between number of overwintering spruce budworm larvae per branch and 

expected infestation level (from Dorais and Kettela 1982).

Geographic Region No. of larvae per 

whole branch

No. of larvae per 100 

ft2 (9.3 m2) of foliage

Forecasted 

infestation

Maritimes 1-6 - Low

7-21 - Medium

21-40 - High

>40 - Extreme

Ontario 1-25 - Low

26-65 - Medium

>65 - High

Quebec, 

Newfoundland -

1-100

Low

- 101-300 Medium

- 301-650 High

- >651 Extreme

Maine - 0-175 Low

- 176-500 Medium

- 502-1100 High

- >1100 Extreme
135

Spruce Budworm

Choristoneura fumiferana (Clemens) 

Lepidoptera: Tortricidae

Allen, D. C.; Abrahamson, L. P.; Eggen, D. A.; Lanier, G. N.; Swier, S. R.; Kelley, R. 

S.; Auger, M. 1986. Monitoring spruce budworm (Lepidoptera: Tortricidae) 

populations with pheromone-baited traps. Environmental Entomology 15: 152-165.

Objective

To develop a trapping system that would reflect C. fumiferana densities accurately.The 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. A collaborative study was conducted in Canada and the northeastern USA 

to evaluate the effectiveness of four types of pheromone traps and two types of commercial lures as a 

population monitoring tool.

In most locations with most traps, the catch of male C. fumiferana moths was correlated positively with 

second and fourth instar populations. Covered funnel traps (Ramaswamy and Cardé 1982) baited with 

Conrel lures caught significantly more male C. fumiferana moths than those baited with Hercon lures. A 

five-trap cluster placed at least 40 m from an opening with a 40-m interval between traps provided the 

best compromise between sampling accuracy and practicality. Although not tested here, the authors 

recommended field testing of the Unitrap (International Pheromones, London) and the Multi-Pher trap 

(Les Services Biocontrole, Quebec) in further trapping studies because they appeared well designed for 

capturing C. fumiferana moths.

Sampling Procedure

This review describes the use of covered funnel traps. Please consult the original publication for the 

procedures used for other, less effective traps. Age Conrel pheromone lures (96:4 blend of (E)- and (Z)-

11-tetradecenal plus 2% antioxidant) (Albany International, Needham Heights, Massachusetts) for 21 d 

prior to use to reduce trap saturation. Pin one lure in the top of each covered funnel trap (Ramaswany 

and Cardé 1982). At the bottom of each trap, place a killing agent to retain moths entering the trap.

Place a trap cluster every 60-m in the area of concern, 4-7 d prior to initiation of C. fumiferana moth 

flight. Clusters should be placed >40 m from an opening (field, meadow, etc.). Place one trap in each 

of the north, east, south, and west aspects, 40 m from a central trap, for a total of five traps per cluster. 

Suspend each trap from a branch 2-2.5 m above the ground. Trim all foliage within 30 cm to create 

unobstructed access to incoming moths. Retrieve traps about 7 weeks later, and count the number of 

moths.

Notes It takes 30 minutes to set up a trap cluster. One trap cluster will suffice for monitoring 20 ha of 

susceptible host trees.

Reference

Ramaswamy, S. B.; Cardé, R. T. 1982. Nonsaturating traps and long-life attractant lures for monitoring spruce 

budworm males. Journal of Economic Entomology 75: 126-129.
136

Spruce Budworm

Choristoneura fumiferana (Clemens) 

Lepidoptera: Tortricidae

Dobesberger, E. J.; Lim, K. P. 1983. Required sample size for early instar spruce 

budworm, Choristoneura fumiferana (Lepidoptera: Tortricidae), in Newfoundland. 

Canadian Entomologist 115: 1523-1527.

Objective 

To determine the minimum sample size necessary for estimating the larval population density of C. 

fumiferana at various levels of precision.

Abstract 

The 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. A study was carried out in Newfoundland, Canada to determine the minimum sample 

size needed for accurate estimation of second, third, and fourth instar larval populations.

The observed variances for both whole branch and 45-cm tip samples did not differ significantly with 

those expected from a negative binomial distribution. Similar sample sizes were predicted for both the 

whole branch and 45-cm tip samples. Therefore, the 45-cm tip sample was recommended for sampling 

second to fourth instar C. fumiferana because it is the easiest and cheapest sample size for collecting larvae. 

This sampling method was feasible for estimating C. fumiferana populations exceeding one larva per 45-cm 

branch tip.

Sampling Procedure 

Table 1 describes appropriate sample sizes based on larval density, confidence levels, and sampling 

precision. Select the required number of dominant and codominant balsam fir trees (one branch per 

tree) randomly throughout each area of concern. The sample should be carried out when the majority 

of the budworm population is predicted to be second, third and fourth instar. Cut one 45-cm branch tip 

from the mid-crown of each sample tree, and count the number of budworm either in the field or later 

in the laboratory. If samples are to be processed in the laboratory, then store them in a cooler or freezer 

to reduce the likelihood of larvae molting before the samples can be assessed.

Note 

This sampling method depends on previous knowledge of the larval population density of C. fumiferana

in order to determine the appropriate number of samples to collect.

Table

Table 1. Required number of branch tip samples to estimate the population density of early 

instar (second to fourth instar) larvae of the spruce budworm, Choristoneura fumiferana (Clemens).
137

Confidence level (α)

Mean

density

0.90 0.80 0.70

Standard error of mean Standard error of mean Standard error of mean

0.10 0.15 0.20 0.25 0.10 0.15 0.20 0.25 0.10 0.15 0.20 0.25

0.01 34819 15475 8705 5571 20149 8955 5037 3224 12602 5601 3151 2016

0.10 3683 1637 921 589 2131 947 533 341 1333 592 333 213

1 569 253 142 91 329 146 82 53 206 92 52 33

10 258 115 64 41 149 66 37 24 93 41 23 15

20 240 107 60 38 139 62 35 22 87 39 22 14

30 235 104 59 38 136 60 34 22 85 38 21 14

40 232 103 58 37 134 60 34 21 84 37 21 13

Refer to the original publication for SEM = 0.30

Table 1 reprinted with permission from the Canadian Entomologist, January 15, 2001.
138

Spruce Budworm

Choristoneura fumiferana (Clemens) 

Lepidoptera: Tortricidae

Fowler, G. W.; Simmons, G. A. 1982. Spruce budworm egg mass density on balsam 

fir: low to extreme population levels (Lepidoptera: Tortricidae). Great Lakes 

Entomology 15: 277-286.

Objective 

 

To determine the best sample location to estimate egg mass densities of C. fumiferana.

Abstract 

 

The 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. A study was initiated in Michigan’s Upper Peninsula to determine the best sampling 

locations for C. fumiferana egg masses. Two stands, each consisting of 10 extremely and 10 moderately 

defoliated fir trees, for a total of 40 trees, were sampled in 1979. In 1980, five stands consisting of four 

lightly-defoliated balsam fir and white spruce trees, for a total of 40 trees, were sampled. The live crown 

of each tree was divided into three levels (lower, mid- and upper crown) with each level being divided 

into quadrants representing the north, south, east, and west aspects.

Aspect did not explain a significant proportion of the variation in egg mass density. Overall, the majority 

of egg masses were found in the mid-crown of fir and spruce. In addition, egg mass density at the midcrown position was higher than that of the entire tree. Because of the considerable variation in estimates 

encountered in this study, results should be treated with caution until further studies are conducted.

Sampling Procedure 

 

Begin sampling shortly after C. fumiferana egg deposition is completed (i.e., early August). Randomly 

select the center of each group of trees to be sampled in a representative area of each stand. Groups of 

trees should be located 10-50 m from roads, trails or other groups of trees to be sampled. Trees should 

be ~9-18 m tall with no dead tops. Divide the live crown into lower, mid-, and upper crown levels if 

necessary. With a set of pole pruners, cut the appropriate-sized branch from the mid-point of each 

crown level, lowering the sample carefully to avoid losing any egg masses. Estimate the area of new and 

old foliage, and determine the number and location (new vs. old foliage) of egg masses, for each branch 

sampled. Egg mass density can be expressed by surface area of new versus old foliage, all foliage, etc., 

providing that methods are consistent from sample to sample.
139

Spruce Budworm

Choristoneura fumiferana (Clemens) 

Lepidoptera: Tortricidae

Lynch A. M.; Fowler, G.W.; Simmons, G. A. 1990. Sequential sampling plans for spruce 

budworm (Lepidoptera: Tortricidae) egg mass density using Monte Carlo 

simulation. Journal of Economic Entomology 83: 1479-1484.

Objective 

To develop and contrast three sequential sampling plans to estimate the egg mass density of C. fumiferana.

Abstract

The 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.

Three sequential sampling plans based on the negative binomial distribution were developed for 

estimating C. fumiferana egg mass densities in Michigan’s Upper Peninsula. Each plan classified egg mass 

populations as either low or high based on whole branch samples of balsam fir. Wald’s approximation, 

Monte Carlo estimates of actual values, and Monte Carlo estimates of final values were used to predict 

operational characteristics and average sample numbers for each of three plans. Each model had the 

flexibility to include economic constraints, time or labor constraints, regional C. fumiferana population 

levels, and hazard levels.

The number of samples required in Plan I<II<III. The time and labor costs required for Plan I<II<III. 

Plan II classified stands as having high egg mass densities more often than Plans I or III. Therefore, the 

practicality of each plan is dependent upon the management objectives, available resources, and forest 

values.

Sampling Procedure

Sampling methodologies were described previously by Fowler and Simmons (1982). Count all C. 

fumiferana egg masses on a whole branch taken from the mid-crown of each balsam fir tree sampled. 

Determine the foliated area, and then divide the number of egg masses found by the branch area to 

estimate egg mass density (number of egg masses per 10 m2 of foliage). The number of egg masses 

needed for classifying C. fumiferana populations as low or high approximately doubles from Plan III to 

II and from Plan II to I. For each plan, Wald’s approximate procedure, Monte Carlo estimates of actual 

values, and Monte Carlo estimates for the final test are estimated. 

Reference

* Fowler, G. W.; Simmons, G. A. 1982. Spruce budworm egg mass density on balsam fir: low to extreme 

population levels (Lepidoptera: Tortricidae). Great Lakes Entomology 15: 277-286.
140

Spruce Budworm

Choristoneura fumiferana (Clemens) 

Lepidoptera: Tortricidae

Régnière, J. J.; Sanders, C. J. 1983. Optimal sample size for the estimation of spruce 

budworm (Lepidoptera: Tortricidae) populations on balsam fir and white spruce. 

Canadian Entomologist 115: 1621-1626.

Objective

To determine the sample size required to estimate larval populations of C. fumiferana at 65% and 95% 

confidence levels and at various precision levels.

Abstract

The 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. A study was conducted near Black Sturgeon Lake, Ontario, Canada to determine the 

sample size needed to estimate the population density of third, fourth, and fifth instar C. fumiferana larvae 

on 45-cm branch tips of balsam fir and white spruce.

The distribution of larvae at densities <0.1 larvae per branch tip was nearly random but became 

aggregated at densities greater than 0.2 larvae per tip for both host species. This method of estimation 

works well up to a density of 50 larvae per tip. The suitability of this sampling method for estimating 

late instar larval density is discussed.

Sampling Procedure

To determine the number of samples needed, given the budworm density per 45-cm branch tip and the 

desired precision level, refer to Table 1. Select dominant and codominant balsam fir or white spruce in 

the most representative area(s) of the area of concern. With a set of pole pruners, cut a 45-cm branch 

tip from the mid-crown of each sample tree when larvae are third, fourth, and fifth instar. Place and 

store each branch in a brown paper bag. In the laboratory, examine the buds (and shoots) and record the 

number of larvae.

The sample size needed to achieve a given degree of precision is based on the estimates of the mean (X), 

variance (S2

) and the level of precision desired. When precision is expressed in terms of a confidence 

interval at the (1-α) probability level, the half-width of which is selected as a constant proportion (C) of 

the mean, the optimal sample size (n) is given by

n = Zα / 2

C

 

 

 

 

2 2.08

X

0.75

where zα/2 is the upper α/2 point of the standard normal distribution (under the assumption that the 

sample sizes involved are greater than 30). When sample sizes are small (<30), t can be used as the 
141

standard deviate corresponding to the desired probability level (Student’s t).

Notes

The execution of this sampling plan requires that the user has prior knowledge of the larval density in the 

area(s) to be sampled. The distribution of larvae at higher densities (>50 larvae per 45-cm tip) approximates 

the negative binomial distribution, indicating that the 45-cm branch tip is either not a particularly appropriate 

sample unit or that a different expression of density is required to reduce sample variance.

Table

Table 1. Sample sizes required to achieve various levels of precision relative to the mean, based on 

the standard error of the mean (65% confidence intervals) or on 95% confidence intervals at various 

densities of spruce budworm larvae.

Density 

per 

45 cm tip

Standard error of the mean 95% confidence interval

15% 20% 25% 15% 20% 25%

0.01 2923 1644 1052 11230 6317 4043

0.1 874 492 315 3359 1889 1209

0.25 520 292 187 1997 1123 719

0.5 156 88 56 597 336 215

1.0 92 52 33 355 200 128

2.5 47 26 17 179 101 64

5.0 28 16 10 106 60 38

10.0 16 9 6 63 36 23

Table 1 reprinted with permission from the Canadian Entomologist, January 15, 2001.
142

Spruce Budworm

Choristoneura fumiferana (Clemens) 

Lepidoptera: Tortricidae

Sanders, C. J. 1980. A summary of current techniques used for sampling spruce 

budworm populations and estimating defoliation in eastern Canada. Rep. O-X306. Canadian Forest Service, Great Lakes Forestry Centre; 34 p.

Objectives 

To provide a summary of population level estimates for various life stages of C. fumiferana; to summarize 

techniques for estimating defoliation levels; and to summarize two hazard prediction systems.

Abstract

The 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 by the Canadian Forestry Service and by provincial agencies in Ontario (ONT), Quebec 

(PQ), New Brunswick (NB), Nova Scotia (NS), Prince Edward Island (PEI) and Newfoundland (NF) up 

to 1980 are described for estimating population densities of the eggs, overwintering second instars, large 

larvae, pupae and adults, and defoliation levels. Methods dealing with extensive and intensive surveys are 

described in detail in this report.

The egg, larval and pupal surveys used either a 45-cm branch tip or a whole branch taken either from 

the mid-crown or from each of the upper, mid- and lower crown positions of host trees. Light, Malaise, 

and pheromone traps were used to determine population trends or to detect new budworm moth 

invasions. Tables are provided to assess budworm infestation levels for each survey method presented. 

Four methods were used to evaluate defoliation: aerial assessments, ground assessments with binoculars, 

and two ground assessments methods that are hands on examination of foliage (i.e., the Fettes and the 

Dorais-Hardy methods). A hazard prediction system based on current defoliation, previous damage, 

recovery and egg mass counts was presented.

Sampling Procedure

This document describes procedures specific to extensive and intensive sampling plans within plots only. 

The number of plots to be sampled depends upon the size of the area of concern.

Surveys for eggs: The egg mass survey is used widely for predicting budworm population levels. Egg 

mass sampling should be carried out as soon as possible following the end of oviposition. For extensive 

egg mass surveys, cut a whole branch from the mid-crown of each sample tree. For example, the 

maximum number of branches (trees) sampled at each plot is 5 for PQ, 3 for NB, and 6 for ONT. For 

intensive surveys, cut one whole branch from each of the lower, mid- and upper crown positions.

Estimate carefully the foliated area for each branch sampled. The most accurate method is to multiply 
143

the length of green branch by the width of green branch and divide this product by 2. Examine visually 

the foliage for all egg masses in the laboratory, and remove and count all egg-bearing needles. Keep a 

subsample of the egg-bearing needles to estimate the number of viable eggs per egg mass. UV light 

can also be used to detect egg masses, but this technique is time consuming. Each branch should be 

rechecked by another worker in case some egg masses were missed initially. It takes 60 and 120 minutes 

per branch to check for egg masses on balsam fir and spruce, respectively. Express egg mass density 

as the number of egg masses per 10 square meters pooled for all samples taken per plot. Egg mass 

densities are then classified as light (<25 egg masses/10 m2), moderate (50-100 egg masses/10 m2), or 

severe (>200 egg masses/10m2), or simply as low or high (Table 1).

Surveys for overwintering larvae: This survey is also used widely and tends to be less labor intensive than 

the egg survey. Sample any time from September to April of the next year. Sample a whole branch, or 

a 45-cm branch in PQ, from the mid-crown of host trees in extensive surveys. One branch should be 

sampled in each crown zone during intensive surveys. Five to 14 branches should be sampled per plot, 

depending on the mean to variance ratio (Table 3). Before processing branches to count second instar 

larvae, calculate branch area as described for the egg mass survey.

A sodium hydroxide (NaOH) wash (Miller and McDougall 1968, Miller and others 1971, Miller and 

Kettela 1972) or a forced emergence (Miller 1958), sometimes followed by a NaOH wash in intensive 

surveys, are two methods used widely by to extract second instar budworm from their hibernaculae.

Sodium hydroxide wash: Washes can be conducted any time from September though April of 

the next year. Clip each branch into small pieces and place all in a paper bag labeled by plot, tree 

and branch number. If prolonged storage is necessary the foliage should be kept at 0 °C. Wash 

each sample in a 10 L plastic pail and leave overnight in a warm room to thaw. Add 90 g of 

sodium hydroxide per pail and fill pail to the 9 L mark with 50 °C water to make a 1% solution 

of NaOH. Keep foliage submerged with a weighted screen top. Let soak for 5 h, stirring every 

hour. Strain the liquid content of each pail through two sieves, one with a 0.8 mm mesh and a 

second with a 0.25 mm mesh. Place a wire basket in a tub (90 wide by 150 long by 9 cm deep, 

with a corrugated bottom and drain), and pour the remaining contents of the pail into the 

wire basket removing any larvae stuck to the sides of the pail. Wash foliage in the wire basket 

thoroughly and then discard. At this point, branches should be completely bare. Pour contents 

of the tub through both sieves and wash into a collecting jar. Pour contents of collecting jar, 

removing any larvae stuck to the sides of the jar, into a 5 L separating funnel. Add hexane to 

the funnel, creating a 3 mm layer on top of the aqueous solution. Shake this mixture vigorously 

to obtain thorough mixing and allow 5 minutes to settle. Approximately 99% of the larvae will 

settle at the hexane-water interface. Draw off plant debris that has settled at the bottom of the 

funnel, and draw off the hexane-water fraction into 400 ml beakers to be vacuum filtered. If 

there is much plant debris in this fraction, process only 100 ml at a time. Fit a Buchner funnel 

to the separating filter using a molded rubber diaphragm (Filtervac) and connect a filter pump. 

Pour debris onto a piece of grided (to be seen under microscope), wetted filter paper. ‘Washed’ 

budworm larvae have black heads and very light colored bodies. This technique requires 5 h 

soaking time and 30 min per branch for preparation and examination. Miller and others (1971) 

found the cost of this technique to be substantially higher than the beat method for large 

budworm larvae but only a third of the cost of counting egg masses.

Forced emergence: Diapause must be complete before this technique can be used. This usually 
144

occurs in early March in eastern Canada, however, samples can be collected earlier and stored 

until diapause requirements have been met. The enclosed box or paper cone methods can be 

used.

Any sealed, darkened container with a transparent, clear collecting vial would make an adequate 

emergence cage for the enclosed box method. Fill each container with foliage but do not pack 

tightly. Orient box so that the collecting vial is pointed upward, facing a bank of lights that 

serve to attract larvae to this vial. Count and remove the larvae in the collecting vials periodically 

during the emergence period. A modification of this technique is used in PQ. A 45-cm branch 

tip is placed in a small polystyrene ice bucket with a closed top. The bucket is painted black to 

reduce light transmission. A transparent vial is fitted into the bottom of each bucket, and all 

buckets are placed on a wire fence with the vials facing the light. Vials are checked for 10 d, after 

which time budworms have become third and fourth instars. Branches are then removed from 

the buckets, and the number of larvae remaining in the bucket are counted. Alternatively, branch 

tips can be left in the bucket (with no clear vial) until most larvae are either third or fourth instar, 

and then the foliage can be removed and beaten (as for large larvae below) to dislodge all larvae.

The paper cone method involves wrapping branches with paper towel and hanging each branch 

separately by the proximal end, suspending all samples by a string under a strong light. Collect 

larvae as they crawl up the paper and string. Some larvae will drop off the branch, so to collect 

these larvae place a piece of paper below each branch. Ring the edge of each sheet of paper 

with Tanglefoot (The Tanglefoot Co., Grand Rapids, MI) to prevent escape. The string is also 

ringed with Tanglefoot approximately 30 cm up from the branch. Spray branches with water 

periodically to prevent drying. This method is messier (due to Tanglefoot), and requires more 

time and space, than the box method. Information on relationships between the density of 

overwintering second instar budworm and the population level of large larvae, or of defoliation 

potential, can be found in Miller and others (1971) and Miller and Kettela (1972).

Surveys for large larvae: To determine how many samples are needed for the large larvae survey, see 

either Table 4, 5, or 6 (original publication) depending on the chosen survey method or method of 

expressing budworm population levels. Sampling should coincide with the predicted peak of the third 

through sixth instar stages. Collect a 45-cm branch tip or a whole branch for extensive or intensive 

surveys, respectively. For extensive surveys, remove one branch tip from the mid-crown of each sample 

tree. For intensive surveys, remove one whole branch from each crown level of each sample tree if 

population levels appear moderate to high. If populations appear low, remove one whole branch from 

the mid-crown of each sample tree. If trees have been sprayed with insecticides, then remove branches 

from both the upwind and downwind sides of the tree.

In the laboratory, count the number of current shoots to determine the potential number of feeding 

sites. Examine visually the foliage for presence of large larvae, otherwise, larvae can be extracted from 

foliage by a drum or a beating technique. Third and fourth instars are usually found in buds or staminate 

flowers but fifth and sixth instars can be found anywhere on the branch, including the bag where the 

branch was stored.

To sample fifth and sixth instars in the field, use either a basket attachment below the cutting head of the 

pole pruners, or a tarp below the branch being lowered from the sample tree. Populations are classified 

as either low or high based on the cumulative counts of budworm from 45-cm branch tips (Table 7). 

The drum technique, a method to separate larvae from foliage, is summarized as follows:
145

The beating technique is used to obtain indices of population density for extensive surveys. Beat with a 

stick 1 m3 of foliage at ground level from two sides of each of 10 trees (20 samples total), counting the 

number of larvae falling onto a 1-m2 cloth tray situated below the ‘beat’ area. For larval densities of less 

or greater than 5 per cubic meter, refer to Table 8 in the original publication to determine the number of 

additional samples needed. If densities are less than 1 larva per cubic meter then sample until one larva 

is found.

Surveys for pupae: Sample during the predicted peak pupal stage of the population or shortly after adult 

emergence. Collect either a 45-cm branch tip or a whole branch for extensive and intensive surveys, 

respectively. The sampling intensity and method of branch examination is the same as the large larval 

survey. The drum technique may also be used in extensive surveys, but this technique damages some 

pupae and is not recommended if pupae are to be reared to adults. Populations are classified as either 

low or high based on the cumulative counts of pupae from 2-10 45-cm branch tips per sample tree (Table 

9).

Surveys for adults: Light, Malaise, and pheromone traps are used to develop population indices for 

budworm moths. Light traps can be used to forecast population trends for a period of years in the same 

location and can also be used to indicate moth invasions into new areas. Miller and others (1979) found 
146

that catches of female moths in light traps suspended in the forest canopy, coupled with density estimates 

of resident female pupae, can be used as a crude estimate of budworm egg mass densities over a broad 

area. This method is considerably cheaper than egg mass surveys. As of 1980, Malaise and pheromone 

traps were not in operational use as tools to determine budworm population levels.

Surveys to estimate defoliation: Budworm defoliation can be determined by aerial, ground with 

binocular, Fettes and Dorais-Hardy assessments. Aerial assessments can be made from aircraft with 

reasonable accuracy by trained observers (Waters and others 1958). Use binoculars from <50 m on the 

ground to determine the percentage of new growth remaining in the upper two-fifths of the live crown. 

Rate trees as excellent (>75% new growth remains), very good (50-75%), good (25-50%), poor (<25%), 

very poor (only if some new shoots are found on entire crown) or nil (0%).

The Fettes method (Fettes 1950) involves obtaining branches from the mid-crown of balsam fir and then 

visually estimating the percentage of needles removed from each current-year shoot on the branch (Fig. 

2 in original publication). Estimates are averaged to provide a defoliation level for the whole branch. 

The Dorais-Hardy method is used for branches that were so defoliated that normal bud development 

was prevented. This method accounts for damage to buds and foliage, however, they are not cut from 

the tree as in the Fettes method. Instead they are labeled and defoliation levels assessed both before 

and after an insecticide treatment. Before treatment, record the presence of terminal buds on the three 

terminal shoots of the branch (Fig. 3 in the original publication). Also, estimate defoliation, based on the 

Fettes method, for each of the three terminal shoots, and then record the average of the three estimates. 

Because a new year of growth has been added in the time between the before and after treatment 

estimates of defoliation, the new set of buds must be evaluated for presence as well as the same shoots 

evaluated earlier (bottom of Fig. 3 in the original publication). An index can be calculated from this 

data that indicates recovery potential. However, the Dorais-Hardy method does not account for the 

production of adventitious buds, which may be produced by heavily defoliated trees.

Hazard mapping: A method of assessing hazard is described in Prebble (1975). Determination of 

hazard is based on four criteria: egg mass counts, current defoliation, previous damage, and recovery; 

the last 3 of which can be estimated when conducting the egg survey. Each criterion is then assigned a 

numerical value and each value is added to determine hazard levels (Table 10). Most reliance is placed on 

the egg mass counts because they predict the severity of defoliation and damage for the following year. 

If egg mass density in a given area is 0 (nil), 1-99 (light), 100-239 (moderate), 240-399 (severe) or >400 

(extreme) eggs per square foot, then assign the appropriate hazard value (Table 10).

Defoliation is assessed during the ground survey but can also be estimated during aerial surveys, or a 

combination of both techniques. Estimate defoliation using the Fettes method (Table 10). Assign a nil 

(0%), light (1-25%), moderate (25-65%), and severe (>65%) rating as appropriate.

Examine the defoliation of each age class of foliage and assess the health of the crown of each 

tree sampled for egg masses to estimate previous damage. Assign a nil (no apparent damage), light 

(defoliation evident on 1-year old shoots), moderate (defoliation evident on 1- and 2-year-old shoots and 

crown appearing thin) or severe (crown noticeably thin and grayish with >60 cm of bare top) hazard 

value.

Trees weakened by budworm attack vary in their ability to recover. Assign a nil (no current shoots), poor 

(a small crop of current shoots), fair (moderate crop of current shoots) and good rating (current foliage 
147

crop apparently normal) to each tree sampled as appropriate to define recovery.

Once the hazard value has been determined for each of the four criteria above, add each value to 

determine overall hazard levels. Assign a low (0-7), moderate (8-10), severe (11-14) and extreme(>15) 

rating as appropriate. These data can then be plotted on appropriate maps to help identify the areas in 

most need of control measures.

Quebec uses a similar method of hazard determination except that only the current year egg mass survey 

data and a 4-yr defoliation history are used to determine hazard levels that would provide justification of 

control measures.

Note

Please refer to original publications for more details regarding these survey methods.

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 Lab; 11 p.

Miller, C. A. 1958. The measurement of spruce budworm populations and mortality during the first and second 

larval instars. Canadian Journal of Zoology 36: 409-422.

Miller, C. A.; McDougall, G. A. 1968. A new sampling technique for spruce budworm larvae. Res. Note 24. 

Ottawa: Canadian Forest Service, Rural Development; 30-31.

Miller, C. A.; Kettela, E. G.; McDougall, G. A. 1971. A sampling technique for overwintering spruce budworm and 

its applicability to population surveys. Inf. Rep. M-X-25. Fredericton, NB: Canadian Forest Service; 4 p. 

Miller, C. A.; Kettela, E. G. 1972. An additional note on sampling overwintering spruce budworm larvae. Inf. Rep. 

M-X-34. Fredericton, NB: Canadian Forest Service; 6 p.

Miller, C. A.; Greenbank, D. O.; Kettela, E. G. 1979. Possible use of canopy light traps in predicting spruce 

budworm egg-mass counts. Res. Notes 35. Ottawa: Canadian Forest Service; 29-30.

Prebble, M. L. 1975. Aerial control of forest insects in Canada. Ottawa: Canadian Forest Service; 330 p.

Waters, W. E.; Heller, R. C.; Bean, J. L. 1958. Aerial appraisal of damage by the spruce budworm. Journal of 

Forestry 56: 269-276.
148

Tables

Table 1. Sequential sampling of spruce budworm eggs. Sample unit is one mid-crown branch per tree 

from balsam fir.

No. of sample units

Balsam fir

Population category

Low High

Cumulative egg-masses per 100 ft2

 (~10 m2)

1 --- 313 or more

2 138 or less 469

3 293 624

4 448 779

Table 3. Tentative estimates of variance-mean relationships for overwintering second-instar larvae on 

mid-crown branches of balsam fir, and required sample size for 20% precision.

Mean Variance Required no. of branches

2 2.3 14

4 5.4 8

6 10.5 7

8 17.1 7

10 25.0 6

>10 --- 5
149

Table 7. Sequential sampling of spruce budworm larvae developed for New Brunswick. Sample unit is 

one 45 cm tip per tree from fir, and one from spruce.

Number of 

sample units

Balsam fir Red spruce

Population category Population category

Low High Low High

(Cumulative larvae) (Cumulative larvae)

1 --- 28 or more --- 34 or more

2 --- 36 --- 47

3 2 or less 43 3 or less 60

4 9 50 7 74

5 16 58 11 87

Table 9. Sequential sampling of spruce budworm pupae developed in New Brunswick. Sample unit is 

two 45 cm tips per tree from balsam fir, and four from red spruce (Picea rubens Sarg.).

Number of 

sample units

Balsam fir Red spruce

Population category Population category

Low High Low High

Cumulative pupae Cumulative pupae

1 --- 33 or more --- 9 or more

2 1 or les 50 1 or less 12

3 5 66 4 15

4 10 83 8 19

5 14 100 11 23
150

Table 10. Hazard values assigned to tree condition and budworm abundance, New Brunswick (from 

Prebble [1975]).

Category Hazard value Category Hazard value

Current defoliation Recovery

Nil 0 Good -3

Light 1 Fair -2

Moderate 2 Poor -1

Severe 3 Nil 0

Extreme 4 --- ---

Previous damage Egg-mass density

Nil 0 Nil 0

Light 3 Light 1

Moderate 6 Moderate 2

Severe 9 Severe 3

Extreme 4

Table reproduced from Sander (1980) with permission from Natural Resources Canada, 

Canadian Forest Service, copyright January 15, 2001, Government of Canada. 
151

Spruce Budworm

Choristoneura fumiferana (Clemens) 

Lepidoptera: Tortricidae

Simmons, G. A.; Fowler, G. W. 1984. Considerations when sampling spruce budworm 

egg masses on balsam fir in the Lake states: low to extreme population levels. 

Great Lakes Entomology 17: 87-95.

Objectives 

To examine the effects of sampling balsam fir branches from different areas of same tree; to compare 

effects of a range of egg mass densities in terms of bias and variance; and to examine the influence of 

branch size on accuracy and precision of egg mass density estimation.

Abstract 

The 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 from 5-10 years. A study was carried out in five spruce-fir stands in Michigan’s Upper Peninsula 

to study egg mass densities and distributions.

There was considerable tree to tree and plot to plot variation in egg mass densities, which resulted in high 

sampling error. However, the most optimal sample unit in terms of accuracy and precision was to select 

two to four whole branches from the mid-crown position of each tree.

Sampling Procedure: Select and remove with pole pruners two to four whole branches randomly from 

the mid-crown position of balsam fir. Count and record the number of egg masses and measure the 

foliated area of each branch sampled. Egg mass density is expressed as either the number of egg masses 

per square meter or the number of egg masses per branch.

Notes 

This paper is third in a series of papers that attempt to improve egg mass sampling techniques (Fowler 

and Simmons 1982, Simmons and Fowler 1982). Even though sampling from the mid-crown yields the 

most precise and accurate estimates, the distortion of probability statements is maximized.

References

* Fowler, G. W.; Simmons, G. A. 1982. Spruce budworm egg mass density on balsam fir: low to extreme high 

levels. Great Lakes Entomology 15: 277-286.

Simmons, G. A.; Fowler, G. W. 1982. Spruce budworm egg mass density on balsam fir and white spruce: low 

population levels. Great Lakes Entomology 15: 287-296.
152

Spruce Budworm

Choristoneura fumiferana (Clemens) 

Lepidoptera: Tortricidae

Waters, W. E. 1974. Sequential sampling applied to forest insect surveys. In: 

Proceedings of IUFRO/SAF/SUNY symposium on monitoring forest 

environment through successive sampling. June 24-26; Syracuse, NY; 290-311.

Objective 

To develop a sequential sampling plan for prediction of second and third instar C. fumiferana densities.

Abstract 

The 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 of C. fumiferana cause most of the defoliation. Periodic outbreaks occur every 30 years 

and epidemics can last 5-10 years. A study was carried out in northern Maine to develop a sequential 

sampling plan for second and third instars. These surveys can be used to supplement egg survey 

information, which is the standard means of predicting budworm population levels.

Five 38-cm branch tips collected in the mid-crown of each of five trees per survey point was the 

recommended sample size for this survey. The density of budworm per 38-cm twig needed to classify 

infestations as light, medium, and heavy was <5, 10-15 and >20, respectively. If after sampling 25 twigs a 

decision is not met, then select the classification level closest to the density estimate (Fig. 3).

Sampling Procedure

Select sample points over a representative portion of the area of concern. Cut five 38-cm twigs 

from each of five trees at each survey point. Search the staminate flowers, needles, and closed buds 

for budworm larvae. Once the cumulative number of budworm found reaches a decision boundary 

discontinue sampling. If 25 twigs are examined without a decision, sampling discontinues and the 

decision line nearest the count is chosen (Fig. 3).
153

Figure:

Figure 3 reprinted with permission from the IUFRO/SAD/SUNY symposium, January 

15, 2001.
154

Spruce Budworm 

Choristoneura fumiferana (Clemens) 

Lepidoptera: Tortricidae

Wilson, L. F. 1959. Branch “tip” sampling for determining abundance of spruce 

budworm egg masses. Journal of Economic Entomology 52: 618-621.

Objective 

To develop an efficient and accurate sampling technique, based on area stratification, for estimating C. 

fumiferana egg mass densities on balsam fir, Abies balsamea (L.) Mill.

Abstract 

The spruce budworm, Choristoneura fumiferana (Clemens), is the most destructive defoliator of balsam 

fir and white spruce, Picea glauca (Moench) Voss, in eastern North America. The last three larval instars 

(i.e., fourth, fifth and sixth) cause most of the defoliation. Periodic outbreaks occur every 30 years 

and epidemics can last 5-10 years. A study was carried out in Superior National Forest, Minnesota to 

determine if reliable estimates of egg mass density could be obtained by conducting partial samples 

based on area stratification. This sample plan is a modification of methods developed previously 

whereby sample sizes are decreased without reducing greatly the accuracy.

The majority of egg masses were found within the first 8 cm of new growth. Thus, the technique called 

for separating the tips of the shoots from the rest of the branch. The number of egg masses per tip (Y) 

was related positively to the number of egg masses per branch (X) (Y = 1.167X-0.055) (R = 0.99, P = 

0.05, n = 145). To adjust the density of budworm egg masses on branch tips to reflect the density of 

egg masses per branch, add 16% to the total number of egg masses per tip. Depending upon the size of 

the branch, the time saved by this technique ranges from 25-40% over previous techniques. Branches of 

all sizes from all crown levels can be examined using this technique. However, this sample method is not 

feasible on branches with severe or complete defoliation because females ovipositing on these branches 

tend to place their eggs adjacent to the main stem.

Sampling Procedure 

Cut either a 38-cm long branch tip or a whole branch from the live crown of balsam fir. Refer to the 

description below and Fig. 1 for a graphical representation of the stratification procedure (letters A-F 

correspond with those in Fig. 1). 

A. Remove a balsam fir branch.

B. Cut nodal branchlets from the main stem and put aside for further treatment.

C. Remove the terminal shoot 10 cm below the main bud if foliated, and 10 cm below the defoliated 

area if defoliation has occurred. Put aside for counting of egg masses.

D. Cut short internodal branchlets (2.5 – 12.5 cm) at their center and put aside for counting. Cut long 

internodal branchlets in same way as nodal branchlets and put aside for further treatment. Discard 
155

remaining foliage.

E. Assemble nodal and long internodal branches (see Fig. 1).

F. Cut apical twigs from nodal and internodal branches; cut lateral twigs less than 7.5 cm in length at their base and 

lateral twigs greater than 7.5 cm at their center. Put aside for counting of egg masses. Discard remaining foliage.

Count and record the number of egg masses for each sample. Sample an appropriate number of trees 

per stand to give an average representation of the whole stand.

Note 

This sampling plan is based merely on 145 samples taken from all parts of the live crown and from different sized branches. Therefore, use this plan with caution.
156

Figure

Figure 1 reprinted with permission from the Journal of Economic Entomology, January 

15, 2001.
157

Western Spruce Budworm

Choristoneura occidentalis Freeman 

Lepidoptera: Tortricidae

Carolin, V. M.; Coulter, W. K. 1972. Sampling populations of western spruce budworm 

and predicting defoliation on Douglas-fir in eastern Oregon. Res. Pap. PNW149. Portland: U.S. Department of Agriculture, Forest Service, Pacific Northwest 

Forest and Range Experiment Station; 38 p. 

Objectives 

To develop a method of predicting defoliation of current growth Douglas-fir; and applying this method in 

area-wide surveys.

Abstract 

The western spruce budworm, Choristoneura occidentalis Freeman, is an important pest of Douglas-fir, 

Pseudotsugae menziesii (Mirb.) Franco), true firs, Abies spp., Englemann spruce, Picea englemannii Parry ex. 

Englem., and larch, Larix occidentalis Nutt., in the western USA and Canada. Infestations in mature stands 

cause growth loss, top kill, and occasional tree mortality. Douglas-fir that is defoliated severely or top-killed is 

often subsequently attacked by the Douglas-fir beetle, Dendroctonus pseudotsugae Hopkins.

A compilation of studies on sampling C. occidentalis in Oregon is presented for developing forecasting methods 

to predict defoliation levels of current growth Douglas-fir. Budworm density in non-feeding stages was 

tested for predicting the density of larvae in buds and the subsequent defoliation level. In addition, egg mass 

density was evaluated as an index of defoliation. Sampling cost and efficiency was emphasized in selecting 

appropriate sample units, sizes, and allocation of samples.

A cluster design appeared to be the best solution for sampling non-feeding stages. For low, medium, and high 

populations, number and size of clusters per stand, and number of stands, were determined with a sampling 

error of 20% of the mean, and also for other precision levels. Sampling sizes were particularly large for low 

egg and medium larval densities. Tables were developed using regression equations to show egg mass density, 

corresponding larval density, and expected degree of defoliation.

Sampling Procedure 

The sampling procedures described in this paper are detailed and lengthy. We recommend you refer to the 

original publication for specific information on how to use these techniques in the field. In general, sample 

dominant and codominant Douglas-fir in second-growth stands. For egg sampling, collect one whole branch 

from the mid-crown by climbing. For larval sampling, collect four 38-cm twigs with a 10.7 m pole-pruner for 

each designated crown level. Locate and record the number of eggs and larvae. Multistage analysis, involving 

variance and costs, is used to determine optimum size and allocation of samples for these life stages.

Notes

 

The predictive relationships are based on initial estimates of egg mass density and are specific to Douglasfir in the Blue Mountains of Oregon. The relationship between egg mass and larval density is likely to vary 
158

among regions as evidenced by the fact that egg mass size varies accordingly (Carolin and Honing 1972).

Reference

Carolin, V. M. Jr.; Honing, F. W. 1972. Western spruce budworm. Pest Leafl. 53. Washington, D. C. US Department 

of Agriculture, Forest Service; 8 p.
159

Western Spruce Budworm

Choristoneura occidentalis (Freeman) 

Lepidoptera: Tortricidae

Cole, W. E. 1960. Sequential sampling in spruce budworm control projects. Forest 

Science 6: 51-59.

Objective

To develop a sequential sampling plan to estimate populations of C. occidentalis larvae before and after 

control techniques are applied.

Abstract

The western spruce budworm, Choristoneura occidentalis (Freeman), is the most destructive defoliator of 

Douglas-fir, Pseudotsuga menziesii (Mirb.) Franco in northwestern North America. The last three larval 

instars of C. occidentalis cause most of the defoliation of Douglas-fir. Epidemic populations can last 5-10 

years in duration.

Intensive sampling of C. occidentalis is often required to determine if control measures are warranted and 

to determine if control was successful. A sampling method was developed to predict pre- and post-spray 

population levels of C. occidentalis. The pre-spray larval count was obtained by determining the number 

of larvae on a 38-cm twig collected from each of five trees per plot, on each of 10 plots. A total of 50 

twigs was sampled. Populations were classified as Class I (light, ≤2 larvae per twig), Class II (medium, 

3-5 larvae per twig) or Class III (heavy, ≥6 larvae per twig) prior to treatment. The post-spray larval 

count was obtained by sampling two twigs per tree in the same plots as the pre-spray count, for a total of 

100 twigs sampled. Control was classified as either successful or unsuccessful if ≤0.35 or ≥0.50 larvae 

were found per twig, respectively. This type of sequential sample is suspected to improve predictions of 

C. occidentalis population levels before and after control is applied.

Sampling Procedure

To conduct the pre-spray survey, establish a plot every 100 m, for a total of 10 plots, along a transect 

running perpendicular to the proposed spray swath or across elevation contours in the block. The 

transect(s) should be placed in an area that is representative of the entire spray block. Collect one 38-cm 

twig from the mid-crown of each of five Douglas-fir trees per plot for each of 10 plots per transect line, 

for a total of 50 twigs sampled. Count the number of larvae found on each twig, adding the counts of 

successive twigs to the total. After sampling five twigs, compare the cumulative number of C. occidentalis

larvae found with the values listed in Table 3. If the count falls between those listed for each class, then 

sample another five twigs and compare the cumulative count to the values listed in Table 3. Continue 

until a class designation is determined. If all 50 twigs have been sampled and no decision has been 

reached, then the infestation level is a combination of the two classifications (i.e., light-medium).

To conduct a post-spray survey, use the plots sampled for the pre-spray survey. Wait at least 10 days 

post-spray before conducting the sample to ensure maximum mortality of C. occidentalis larvae. Collect 

a maximum of two twigs from each of the sample trees, for a total of 100 twigs sampled. After the 

number of living larvae has been determined from the first 15 twigs, compare this number with the 
160

values listed in Table 4. Continue sampling, five twigs at a time, until the cumulative count falls below 

(satisfactory control) or rises above (unsatisfactory control) the numbers listed in Table 4. If after 

sampling 100 twigs no decision is reached, then the infestation is classified as being equal to 95% 

reduction.

Both types of sequential samples have a sampling error of 10%. The number of samples, sample trees 

and plots can be varied according to time and labor constraints.

Notes

Areas to be sprayed are selected by some other types of surveys, such as egg or overwintering second 

instar surveys (Wilson 1959, Waters 1974). The pre-spray sample occurs before the most damaging, 

fourth, fifth, and sixth instar C. occidentalis are present in the population.

References

* Wilson, L. F. 1959. Branch “tip” sampling for determining abundance of spruce budworm egg masses. Journal 

of Economic Entomology 52: 618-621.

* Waters, W. E. 1974. Sequential sampling applied to forest insect surveys. In: Proceedings of IUFRO/SAF/

SUNY symposium on monitoring forest environment through successive sampling. June 24-26, Syracuse, 

NY; 290-311.

Tables:

Table 3. Sequential table for field use in precontrol sampling of spruce budworm larval populations.

No. of

twigs

Cumulative number of budworm larvae

Class I

Class I

Continue sampling

Class II

Class II

Class II

Continue sampling

Class III

Class III

5 5 19 27 48

10 17 32 34 75

15 30 44 62 103

20 42 56 89 130

25 54 68 1165 158

30 67 81 144 185

35 79 93 171 212

40 91 105 198 240

45 103 118 226 267

50 116 130 253 294
161

Table 4. Sequential table for field use in postcontrol sampling of spruce budworm larval populations.

Number of 

twigs

examined

Cumulative number of budworm larvae

Satisfactory vs. Unsatisfactory

15

Satisfactory Control

---

Continue Sampling

12

Satisfactory Control

20 2 14

25 4 17

30 6 19

35 8 21

40 11 23

45 13 25

50 15 27

55 17 29

60 19 31

65 21 34

70 24 36

75 26 38

80 28 40

85 30 42

90 32 44

95 34 46

100 36 48

Tables reprinted from Forest Service, Vol. 6 (pgs. 51-59) published by the Society of 

American Foresters, 5400 Grosvenor Lane, Bethesda, MD 20814-2198. Not for further 

reproduction.
162

Western Spruce Budworm

Choristoneura occidentalis Freeman 

Lepidoptera: Tortricidae

Harris, J. W. E. 1977. Egg-sampling for western spruce budworm on Douglas-fir. Res. 

Notes 33. Canadian Forest Service; 26-27.

Objective

To determine if sample unit size and crown level explained a significant proportion of the variation in 

egg mass density. 

Abstract

The western spruce budworm, Choristoneura occidentalis Freeman, is an important pest of Douglas-fir, 

Pseudotsugae menziesii (Mirb.) Franco), true firs, Abies spp., Englemann spruce, Picea englemannii Parry ex. 

Englem., and larch, Larix occidentalis Nutt., in the western USA and Canada. Infestations in mature stands 

cause growth loss, top kill, and occasional tree mortality. Douglas-fir that is defoliated severely or topkilled is often subsequently attacked by the Douglas-fir beetle, Dendroctonus pseudotsugae Hopkins.

Traditional methods of sampling C. occidentalis egg masses involve a choice of branches, or parts of 

branches, from different crown levels. Carolin and Coulter (1972) used a sample unit of 61-cm long 

branch tips, but did not test the reliability and accuracy of smaller sizes that would reduce sampling 

costs. A 25-cm branch tip, 46-cm branch tip, and a longitudinal half of each branch were compared 

for estimating egg mass density in the lower, mid- and upper crown. There were significant betweentree, between-crown level, and between–sample unit differences indicating no single sample unit could 

provide absolute estimates of whole-tree populations. The 25-cm branch tips yielded comparable 

results at all crown levels. However, they were subject to zero counts at low density levels. The authors 

recommended the use of the 46-cm branch tip sample from either of the upper two crown levels.

Sampling Procedure

Remove two branches from each of 20 trees per plot (mid- or upper crown), and count and record 

the number of egg masses per 46-cm branch tip. Branch length should be measured from the base 

of the foliage to the tip. Branch width is measured perpendicular from the midrib to the outermost 

edge. Estimate foliated area per branch by dividing the product of length and width by two. Determine 

the number of egg masses per 0.645 m2. The number of samples can be reduced to 15 trees if three 

branches are examined, or 10 trees if five are examined.

Reference

* Carolin, V.M. and W.K. Coulter. 1972. Sampling populations of western spruce budworm and predicting 

defoliation on Douglas-fir in eastern Oregon. Res. Pap. PNW-149. Portland: U.S. Department of 

Agriculture, Forest Service, Pacific Northwest Forest and Range Experiment Station; 38 p.
163

Western Spruce Budworm

Choristoneura occidentalis Freeman 

Lepidoptera: Tortricidae

Mason, R. R.; Wickman, B. E; Paul, H. G. 1989. Sampling western spruce budworm 

by counting larvae on lower crown branches. Res. Note PNW-486. Portland, OR: 

U.S. Department of Agriculture, Forest Service, Pacific Northwest Research 

Station; 8 p.

Objectives

To develop a nondestructive method of sampling C. occidentalis larvae after they have abandoned the 

buds; and to determine its suitability as an alternative to mid-crown estimates.

Abstract

The western spruce budworm, Choristoneura occidentalis Freeman, is an important pest of Douglas-fir, 

Pseudotsugae menziesii (Mirb.) Franco), true firs, Abies spp., Englemann spruce, Picea englemannii Parry ex 

Engelm., and larch, Larix occidentalis Nutt., in the western USA and Canada. Infestations in mature stands 

cause growth loss, top kill, and occasional tree mortality. Douglas-fir that is defoliated severely or topkilled is often subsequently attacked by the Douglas-fir beetle, Dendroctonus pseudotsugae Hopkins. 

A technique is described for sampling C. occidentalis larvae after bud flush by beating three 45-cm 

branches in the lower crown. Sample data were collected from 32 plots representing a wide range of 

budworm densities, and indicated that larvae were less aggregated in the lower crown than at the same 

density in the mid-crown. In an independent sample of 12 plots, estimates of larval density in the 

mid-crown were 2.5 times higher than, and correlated positively with, density estimates in the lower 

crown. Sampling the lower crown is more efficient and cost-effective than sampling the mid-crown, and 

therefore was recommended for estimating larval densities of C. occidentalis. For most situations, 25-50 

trees will yield a reliable density estimate. Lower crown larval densities were converted to mid-crown 

densities by multiplying by a factor of 2.41.

Sampling Procedure

Time samples to occur after bud flush, and to coincide with the predicted peak abundance of third 

through fifth instar larvae (Beckwith and Kemp 1984, Wickman 1988). Sample Douglas-fir and grand 

fir, A. grandis (Dougl.) Lindl., without preference, except each tree must have foliage that can be reached 

from the ground. Beat three 45-cm branch tips against a handheld dropcloth to dislodge larvae. Each 

branch should be rapped 10-12 times, and the total number of larvae recorded for the three branch 

sample. Determine the mean number of larvae per sample unit for each plot by dividing the sum of all 

larvae found in the three branch samples by the number of trees sampled.

The number of trees n needed for different levels of precision can then be estimated using Fig. 3. Multiply the number of larvae per sample unit by 3.10 to adjust to the number of larvae per square meter 

(Mason 1987). For most situations, sampling 25-50 trees will yield reliable estimates. 
164

References

Beckwith, R. C.; Kemp, W. P. 1984. Shoot growth models for Douglas-fir and grand fir. Forest Science 30: 743-746.

* Mason, R. R. 1987. Frequency sampling to predict densities in sparse population of the Douglas-fir tussock 

moth. Forest Science 33: 145-156.

Wickman, B. E. 1988. Seasonal variation of degree-day accumulation in relation to phenology of western spruce 

budworm, Douglas-fir tussock moth, and host trees in northeastern Oregon. Res. Note PNW-482. 

Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station; 11 p.

Figure:

Figure 3. Sample sizes required to estimate larval density on a plot with a standard error of 10, 15, or 20 

percent of the mean.
165

Western Spruce Budworm

Choristoneura occidentalis Freeman 

Lepidoptera: Tortricidae

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.

Objectives

To estimate population densities of C. occidentalis; and to predict defoliation classes for the following year 

using a sequential sampling plan.

Abstract

The western spruce budworm, Choristoneura occidentalis Freeman, is an important pest of Douglas-fir, 

Pseudotsugae menziesii (Mirb.) Franco), true firs, Abies spp., Englemann spruce, Picea englemannii Parry ex. 

Englem., and larch, Larix occidentalis Nutt., in the western USA and Canada. Infestations in mature stands 

cause growth loss, top kill, and occasional tree mortality. Douglas-fir that is defoliated severely or topkilled is often subsequently attacked by the Douglas-fir beetle, Dendroctonus pseudotsugae Hopkins.

A sequential plan is presented for sampling C. occidentalis infestations on Douglas-fir in the Rocky 

Mountains. Population estimates were based on the number of new egg masses per 61-cm branch 

tip, and used to predict defoliation levels the following year. Thresholds for undetectable (<5%), 

undetectable to light (5-35%), light to moderate (35-65%) and moderate to heavy (>65%) defoliation 

were <0.25, 0.275-1.0, 1.5-5.0 and >5.5 egg masses per sample, respectively.

Sampling Procedure

The sampling unit is a 61-cm branch taken from the mid-crown with pole-pruners. The area of the 

sample unit is approximately 0.161 m2

, and can be converted to the number of egg masses per 0.645 m2

of foliage by multiplying by four.

Sample two branches from a minimum of 25 Douglas-fir trees, 15-21 m tall, and without top kill or 

severe defoliation. Branches that brush several live branches when falling from the tree should be 

discarded. Count all new egg masses, which can be differentiated by their erect, transparent, and shiny 

appearance as opposed to older egg masses that appear collapsed, opaque, and dull. After 25 trees have 

been sampled (50 branches), reference the sequential sampling table (Table 2), and continue sampling 

until a decision is met and populations are classified in one of four classes: 

Class New egg masses per 61 cm branch Defoliation prediction

1 ≤0.250 Undetectable (<5%)

2 0.275-1.0 Undetectable to light (5-35%)

3 1.5-5.0 Light to moderate (35-65%)

4 ≥5.5 Moderate to heavy (>65%)
166

Most populations can be classified using 300 samples (150 trees). This sequential plan can also be used 

to estimate current year defoliation with little effort. Collect 100 shoots at random from the foliage 

collected during egg mass counts, and record the number of undamaged shoots. Refer to Table 3 for 

defoliation estimates based on the percent of undamaged shoots (McKnight 1969).

Note 

The sequential plan is intended for use on Douglas-fir in the central and southern Rocky Mountains. It 

may not be applicable to other host species without adjustments for differences in these relationships.

Reference

McKnight, M. E. 1969. Estimating defoliation of Douglas-fir and white fir by the western budworm. Res. Note 

RM-144. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest and 

Range Experiment Station; 2 p.
167

Tables:

Table continues on following page.
168
169

Western Spruce Budworm

Choristoneura occidentalis Freeman 

Lepidoptera: Tortricidae

Shore, T. L.; Alfaro, R. I.; Harris, J. W. E 1988. Comparison of binocular and cut-branch 

methods for estimating budworm defoliation of Douglas-fir. Journal of the 

Entomological Society of British Columbia 85: 15-20.

Objective

To compare observations with binoculars to cut-branch estimates for classifying defoliation levels.

Abstract 

The western spruce budworm, Choristoneura occidentalis Freeman, is an important pest of Douglas-fir, 

Pseudotsugae menziesii (Mirb.) Franco), true firs, Abies spp., Englemann spruce, Picea englemannii Parry ex. 

Englem., and larch, Larix occidentalis Nutt., in the western USA and Canada. Infestations in mature stands 

cause growth loss, top kill, and occasional tree mortality. Douglas-fir that is defoliated severely or topkilled is often subsequently attacked by the Douglas-fir beetle, Dendroctonus pseudotsugae Hopkins.

Defoliation caused by C. occidentalis was estimated on 91 Douglas-fir trees with binoculars and by 

examination of cut-branches. Binocular estimates of defoliation (Y) were related positively to cut-branch 

estimates of defoliation of current-year foliage (X) (Y = -13.3+1.09X, R2

 = 0.67) and of all age classes 

of foliage (X) (Y = -7.1+0.942X, R2 = 0.78). Twelve and 7.5% defoliation of the current year’s foliage 

and foliage of all ages, respectively, was detected by the cut-branch method before any defoliation was 

detected using binoculars. When trees were assigned into broad defoliation classes of light (1-25%), 

moderate (26-65%) and severe (66-100%), as used in forest insect surveys in British Columbia, the results 

agreed 89% of the time for current-year foliage and 68% for foliage of all ages. The binocular method 

was recommended as a quick and useful means of classifying stands into broad defoliation classes, but 

was not suitable if a high degree of precision was needed.

Sampling Procedure

For binocular estimations, scan the upper half of each tree crown using 7 by 50 mm binoculars, and 

separate defoliation estimates to the nearest 5%. For the cut-branch technique, cut two 50-cm branches 

from opposite aspects of the upper half of each tree crown. Assign each shoot a defoliation class based 

on increments of ten percentage points (0, 1-10, 11-20, 21-30, etc.). Average the defoliation estimates of 

both branches to yield a single estimate for each tree.
170

Western Spruce Budworm

Choristoneura occidentalis Freeman 

Lepidoptera: Tortricidae

Srivastava, N.; Campbell, R. W.; Torgersen, T. R.; Beckwith, R. C. 1984. Sampling the 

western spruce budworm: fourth instars, pupae, and egg masses. Forest Science 

30: 883-892.

Objectives

To predict average densities of each life stage of C. occidentalis per square meter of foliage; and to develop 

a sampling program based on a predictive equation linking branch tip estimates to whole-tree and wholeplot densities.

Abstract

The western spruce budworm, Choristoneura occidentalis Freeman, is an important pest of Douglas-fir, 

Pseudotsugae menziesii (Mirb.) Franco), true firs, Abies spp., Englemann spruce, Picea englemannii Parry ex 

Engelm., and larch, Larix occidentalis Nutt., in the western USA and Canada. Infestations in mature stands 

cause growth loss, top kill, and occasional tree mortality. 

Foliage samples were collected from the lower, mid-, and upper crowns of Douglas-fir and grand fir, A. 

grandis (Dougl.) Lindl., in Washington, Oregon, Idaho, and Montana. Populations of C. occidentalis larvae 

were sampled during the fourth instar larval stage and also during the egg and pupal stages. A sampling 

scheme based on a predictive equation that links whole-tree density of C. occidentalis to densities found on 

45-cm branch tips was presented for each life stage.

No significant differences were found between distributions of eggs, larvae or pupae between Douglas-fir 

and grand fir. Mid-crown samples of fourth instars and egg masses were good predictors of density in 

the whole stand. Whole-stand density (WSL , WSE) per square meter of foliage was related positively to 

average density on terminal tips (XM) taken from the mid-crown (WSL = 0.238 (XM), R2 = 0.98; WSE = 

0.82 (XM), R2 = 0.88). Lower crown, terminal tip samples for pupae (XL) were also related positively to 

density of the whole stand (WSP) per square meter of foliage (WSP = 0.629 (XL), R2 = 0.89).

Sampling Procedure

Select a minimum of 15 trees, 7-14 m in height, randomly within a 5-ha plot. Remove two sample 

branches with pole pruners from the mid-crown if sampling fourth instars and egg masses, and lower 

crown if sampling pupae. Branch length should be measured from the base of the foliage to the tip. 

Branch width is measured perpendicular from the midrib to the outermost edge. Estimate foliated 

area per branch by dividing the product of length and width by two ((W * L)/2). After measuring each 

branch, remove a 45-cm terminal tip from the branch. Count and record the number of each life stage 

present.

In dense populations (i.e., 100 fourth instars, 10 egg masses per tip, or 40 pupae per square meter of 

foliage), a precision of ±20% can be obtained by sampling one tip from each of 15 trees (fourth instars), 

48 trees (egg masses), and 9 trees (pupae).
171

Western Spruce Budworm

Choristoneura occidentalis Freeman 

Lepidoptera: Tortricidae

Twardus, D. B. 1985. Surveys and sampling methods for population and damage 

assessment. In: Brookes, M. H.; Bolbert, J. J.; Mitchell, R. G.; Stark, R. W., 

editors. Managing trees and stands susceptible to western spruce budworm. 

Tech. Bull. 1695. Washington, DC: U.S. Department of Agriculture, Forest 

Service; 27-40. 

Objective

To provide a comprehensive review of sampling techniques used to describe C. occidentalis populations 

and defoliation levels. 

Abstract

The western spruce budworm, Choristoneura occidentalis Freeman, is an important pest of Douglas-fir, 

Pseudotsugae menziesii (Mirb.) Franco), true firs, Abies spp., Englemann spruce, Picea englemannii Parry 

ex. Englem., and larch, Larix occidentalis Nutt., in the western USA and Canada. Infestations in mature 

stands cause growth loss, top kill, and occasional tree mortality. Douglas-fir that is defoliated severely 

or top-killed is often subsequently attacked by the Douglas-fir beetle, Dendroctonus pseudotsugae Hopkins. 

A review of sampling techniques used to describe C. occidentalis populations and defoliation levels is 

presented. Defoliation estimates are based on aerial surveys, ground based surveys with binoculars, and 

mid-crown branch samples. The sampling of egg masses, third and fourth instar larvae, late instar larvae, 

and pupae provided reliable estimates of population density.

Sampling Procedure

Defoliation assessments:

Sketch-map surveys: This is the most common aerial survey method used to detect, delineate, and 

provide crude estimates of defoliation levels. Time surveys to occur during peak damage expression, 

which is typically during late July through early August. Surveys are conducted by fixed-wing aircraft at 

speeds of 130-180 km/h several hundred meters above ground. Reference the table below to connect 

the appearance of defoliated stands to actual defoliation from ground surveys.

Aerial defoliation class Appearance from the air Ground defoliation (%)

None No visible change in foliage <10%

Light Light browning of crown 20-40%

Moderate-Heavy

Orange to light brown 

cast to foliage 50-100%

Severe Entire crown appears gray; top kill 

and tree mortality observed

50-100%
172

Aerial photography: This method is used to map and evaluate budworm-caused defoliation, 

which is evident as color changes on the photograph.

Whole-tree or binocular assessment: This method is a ground-based estimate that is subjective, 

but allows for rapid classification of defoliation levels. Divide the tree crown visually into thirds, 

and assign a defoliation code to each level (lower, mid-, upper): 

Class Percent defoliation

1 0

2 1-25

3 26-50

4 51-75

5 76-99

6 100

Express defoliation for each tree as an average of the three levels.

Mid-crown branch samples: This is the most common method used to estimate branch 

defoliation. Clip 46-cm branch samples from the mid-crown of trees 7-14 m tall. Each of 25 

apical shoots per branch is rated using the six-class system described above. Estimates are based 

only on current year’s defoliation.

Population assessments:

Egg-mass sampling: This method is one of the most common techniques used for estimating 

populations, and can be conducted over large areas without excessive time restrictions. Planning 

insecticide treatments requires considerable advanced notice, and egg mass densities are used to 

predict subsequent defoliation for decision-making procedures. The positive linear relationship 

between egg mass density and subsequent infestation class allows egg mass densities to be used 

to predict population density the following year (Carolin and Coulter 1972). Density increases 

with crown height, but equations are available for estimating whole-plot density based on midcrown samples (Srivastava and others 1984).

A sequential sampling plan is available that predicts subsequent defoliation based on new egg 

mass counts obtained from 61-cm branch samples (McKnight and others 1970). Sample a 

minimum of 25 Douglas-fir trees, 15-21 m in height, and without top kill or severe defoliation. 

Collect two branches from each tree, and count all new egg masses. After 25 trees have been 

sampled (50 branches), reference the sequential sampling table (McKnight and others 1970), and 

continue sampling until a decision is met and populations are classified in one of four categories:
173

Class New egg masses 

per 61 cm branch Defoliation prediction

1 ≤0.250 Undetectable (<5%)

2 0.275-1.0 Undetectable to light (5-35%)

3 1.5-5.0 Light to moderate (35-65%)

4 ≥5.5 Moderate to heavy (>65%)

Sampling overwintering larvae: Several methods have been developed, but none receive 

significant application.

Sampling early-instar larvae: Time surveys to coincide with the predicted peak of the third and 

fourth instar stages. Select a minimum of 15 trees randomly, 7-14 m in height, within a 5-ha 

plot. Remove two sample branches from the mid-crown. Branch length should be measured 

from the base of the foliage to the tip. Branch width is measured perpendicular from the 

midrib to the outermost edge. Estimate foliage surface area per branch by dividing the product 

of length and width by two. After measuring each branch, remove a 45-cm terminal tip, and 

count and record the number of larvae present. Srivastava and others (1984) found that wholeplot density (WSL) per square meter of foliage was related positively to average density of C. 

occidentalis per plot (XM) (WSL + 0.238 (XM), R2

 = 0.98).

To conduct a quick sample, collect 45-cm terminal tips sequentially from the mid-crown of each 

tree. Density is classified relative to a predetermined threshold:

Larvae per 45 cm tip Infestation class

0-3 Light

4-7 Moderate

≥8 Heavy

Alternative sampling plans distinguish between light and moderate-to-heavy infestations (Table 

5-5), and light-to-moderate and heavy infestations (Table 5-6).

Sampling late instar larvae: This is the most common method used for evaluating the efficacy 

of insecticide treatment, which is timed near the predicted peak of the fifth instar stage of C. 

occidentalis. Sample three 45-cm branches at the bottom of the crown on each of 25 trees per 

plot. Beat each branch against a hand-held cloth to dislodge larvae, and count and record the 

number of larvae. Refer to Figure 5-5 to relate beat sample counts to the number of larvae per 

square meter of foliage.

Sampling pupae: Pupae are most vulnerable to predation, and therefore samples should be 

conducted after natural mortality has occurred. Select a minimum of 15 trees randomly, 7-14 

m in height, within a 5-ha plot. Remove two sample branches from the lower crown. Measure 

branch area as described above. After measuring each branch, remove a 45-cm terminal tip, and 

count and record the number of pupae present. Whole plot density (WSP) per square meter 

foliage is related positively to the average density per plot (XL) (WSP = 0.629 (XL), R2

 = 0.89) 

(Srivastava and others 1984).
174

Sampling Adults: The attraction of moths to light traps is used to monitor population changes in low 

density infestations. Pheromone-baited sticky traps may have promise for forecasting future stand risks.

Note: This review describes briefly several techniques available for monitoring C. occidentalis populations. 

We refer you to the original publication if more detailed information is desired.

References

* Carolin, V. M.; Coulter, W. K. 1972. Sampling populations of western spruce budworm and predicting 

defoliation on Douglas-fir in eastern Oregon. Res. Pap. PNW-149. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacific Northwest Forest and Range Experiment Station; 38 p. 

* 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.

* Srivastava, N.; Campbell, R. W.; Torgersen, T. R.; Beckwith, R. C. 1984. Sampling the western spruce budworm: 

fourth instars, pupae, and egg masses. Forest Science 30: 883-892.

Figure and Tables

Table 5-5. Sequential classification scheme for separating light from moderate-to-heavy populations of 

fourth instars at 95 percent confidence level (adapted from Srivastava and Campbell 1983 unpubl.).

Number of trees

Cumulative number of budworm larvae1

Light Moderate to heavy

5 <1 >37

10 <10 >64

15 <20 >90

20 <31 >114

25 <43 >138

100 <235 >477

1

 If a count falls between the limits in the two columns, continue sampling
175

Table 5-6. Sequential classification scheme for separating light-to-moderate from heavy populations of 

fourth instars at 95 percent confidence level (adapted from Srivastava and Campbell 1983, unpublished)

Number of trees

Cumulative number of budworm larvae1

Light to moderate Heavy

5 <11 >51

10 <36 >117

15 <64 >165

20 <92 >212

25 <121 >258

100 <592 >917

11If a count falls between the limits in the two columns, continue sampling.
176

Jack Pine Budworm

Choristoneura pinus Freeman 

Lepidoptera: Tortricidae

Batzer, H. O.; Jennings, D. T. 1980. Numerical analysis of a jack pine budworm 

outbreak in various densities of jack pine. Environmental Entomology 9: 514-524.

Objective

To determine if stand density influences the density of C. pinus.

Abstract

The jack pine budworm, Choristoneura pinus Freeman, is an important pest of jack pine, Pinus banksiana

Lamb., and to a lesser extent 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. 

A life table study of C. pinus was superimposed on a stocking level study of dense jack pine in northern 

Minnesota to determine if C. pinus density was related to stand density; and to provide a useful technique 

for sampling C. pinus egg masses. The number of eggs per hectare was estimated from branch samples 

by first expressing them as numbers per meter of foliated branch. Based on the data collected from 205 

felled trees, an equation was developed to estimate total foliated length per tree (Y = 5.54X1

 – 1.45X2 – 

1.11). The number of eggs per egg mass (Y) was related positively to egg mass length (X) when an egg 

mass had two (Y = 5.28X – 12.06, R2 = 0.87) or three (Y = 6.50X – 11.76, R2 = 0.87) rows.

Sampling Procedure

Sample three trees from each 2.5 cm diameter class among those closest to the center of a 400-m2 plot 

(6-18 trees). Clip one branch from the lower crown and another from the upper crown, and be careful 

not to dislodge egg masses. Estimate the number of eggs per ha by first expressing them as numbers per 

meter of foliated branch. Multiply this value by the estimated total foliated length per tree (Y) given by 

the equation:

Y = 5.54X1 – 1.45X2

 – 1.11 (R2 = 0.91)

where Y is in m, X1

 represents diameter at 1.3 m (d.b.h.; cm), and X2 represents tree height (m).

Estimate the number of eggs per mass (Y) by the following regression equations based on egg mass 

length in mm (X) and number of egg rows:

Y = 5.28X – 12.06 (2 egg rows)

Y = 6.50X – 11.76 (3 egg rows)

Three row egg masses were those in which over half the length of the egg mass has more than two rows 

of eggs.
177

Jack Pine Budworm

Choristoneura pinus Freeman 

Lepidoptera: Tortricidae

Foltz, J. L.; Knight, F. B.; Allen, D. C.; Mattson, W. J., Jr. 1968. A technique for sampling 

populations of the jack-pine budworm. Forest Science 14: 277-281.

Objective

To provide a method of sampling C. pinus populations in all life stages except the adult stage.

Abstract

The jack pine budworm, Choristoneura pinus Freeman, is an important pest of jack pine, Pinus banksiana

Lamb., and to a lesser extent red pine, P. resinosa Ait., in the Great Lake region and Canada. Extensive top 

kill is common during outbreaks, but tree mortality is rare unless infestations coincide with periods of 

drought.

Larvae and pupae of C. pinus are more prevalent near branch tips and may be concentrated in one crown 

level. For this reason, a sampling method of estimating population density consists of counting the 

number of budworms and shoot tips on the first 91 cm of two branches from the mid-crown, and two 

branches from the lower crown. The number of budworm on a whole branch (Y1,2) was related positively 

to the average number of budworm found on 100 branch tips (X) in low (Y1

 = 0.52 X + 0.17, R2 = 0.61) 

and high (Y2

 = 1.00 X – 1.34, R2 = 0.99) density populations.

Sampling Procedure

Collect and inspect 40 branches from a sample cluster of 10 trees per stand. Cut two distal 91-cm branch 

tips randomly from the mid-crown and two from the lower crown of jack pine. Count and record the 

number of budworms present in each sample. Express the population as the number of budworm per 

100 tips (X). The population density for the entire branch (Y1

) is calculated by:

Y1

 = 0.52 X + 0.17 for low density populations

Y2

 = 1.00 X – 1.34 for high density populations

Use the first equation if X < 3.146 and use the second equation if X > 3.147. The average density of 

the two branch sample is an estimate of the population density within the crown level. Use the number 

of tips per tree and the number of trees per ha to convert estimated populations densities for the entire 

branch ((Y1

 + Y2)/2) to the number of budworms per ha. To predict the number of tips per tree use the 

following equation:

Log (tips per tree) = 0.06269 (crown diameter (ft)) + 2.84186

Notes

Sampling is conducted in a relatively homogeneous stand of jack pine at least 4 ha in area. For statistics 

concerning the regression equations provided in this review, please consult the original publication.
178

Jack Pine Budworm

Choristoneura pinus Freeman 

Lepidoptera: Tortricidae

Kulman, H. M.; Hodson, A. C. 1962. A sampling unit for the jack-pine budworm, 

Choristoneura pinus. Journal of Economic Entomology 55: 801-802.

Objectives

To identify a sampling unit for estimating larval density of C. pinus, which would also facilitate the 

forecasting of defoliation levels. 

Abstract

The jack pine budworm, Choristoneura pinus Freeman, is an important pest of jack pine, Pinus banksiana

Lamb., and to a lesser extent 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.

A sampling unit for estimating C. pinus larval populations was determined from examination of the distal 

cluster of new shoots. A consistent ratio was found between larval density in the distal cluster of new 

shoots and the population of the next 10 lateral clusters. The number of larvae on the next 10 lateral 

shoots (Y) was related positively to the number of larvae on the distal cluster of new shoots (X) (Y = 

0.459 + 0.475X).

Sampling Procedure

Sample the distal cluster on new shoots of three branches per tree, and count and record the number 

of larvae. There is a consistent relationship between the larval population on the distal cluster of new 

shoots, and the population of the next cluster of older shoots of approximately 3:1. This relationship 

can be described by the equation:

Y = 0.459 + 0.475X

where, X represents the number of larvae on the distal cluster of new shoots and Y represents the 

number on the next 10 lateral shoots.

Notes

The relationship described here was established during low population levels and may not reflect those 

during higher ones. Further studies are needed to determine the number of shoot clusters that would 

need to be sampled to estimate defoliation levels.
179

Jack Pine Budworm

Choristoneura pinus Freeman 

Lepidoptera: Tortricidae

Nealis, V. G.; Lysyk, T. J. 1988. Sampling overwintering jack pine budworm, 

Choristoneura pinus pinus Free. (Lepidoptera: Tortricidae), and two of its 

parasitoids (Hymenoptera). Canadian Entomologist 120: 1101-1111.

Objective

To develop a sampling method for estimating overwintering larval populations of C. pinus. 

Abstract 

The jack pine budworm, Choristoneura pinus Freeman, is an important pest of jack pine, Pinus banksiana

Lamb., and to a lesser extent 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.

Data on the distribution of C. pinus collected between 1985-1987 in northern Ontario, Canada were used 

to develop guidelines for sampling overwintering larvae. Counts of the number of C. pinus per m of 

branch and the number per square meter of branch bark surface area for three crown levels and each 

cardinal direction were conducted. An entire branch was recommended as a sample unit because the 60-

cm branch tip sections underestimated actual densities. The number of larvae per square meter of bark 

surface (Y) was related positively to the number of larvae per m of branch (X) (Y = 110.8 + 29X, R2

 = 

0.92, P < 0.01, n = 250).

Sampling Procedure

Sample one branch randomly from the lower crown of codominant jack pine in late autumn through 

early spring when larvae are still overwintering. Record the branch length and butt diameter to estimate 

bark surface area. Larvae are forced out of the hibernacula using the forced emergence method (Miller 

1958). Place bundles of branches and foliage in paper towels and suspend them over water basins to 

maintain high levels of humidity. Remove emerging larvae and record each larva found on the paper 

towel. Density is expressed as the number of insects per square meter of branch bark surface area. The 

optimal sample sizes for three levels of precision are provided in Table 3.

The relationship between the number of larvae per meter of branch (X) and the number of larvae per 

square meter of bark surface (Y) can be calculated by Y = 110.8 + 29X.

Reference

Miller, C. A. 1958. The measurement of spruce budworm populations and mortality during the first and 

second larval instars. Canadian Journal of Zoology 36: 409-422.
180

Table

Table 3. Number of branch samples required at different levels of precision and increasing density of C. 

pinus, using two methods to express their density.

Density of 

overwintering 

budworm larvae

Precision (%)

90% 85% 80%

Per meter Per m2 Per meter Per m2 Per meter Per m2 Per meter Per m2

5 250 44 34 20 15 11 9

10 400 34 29 15 13 8 7

15 550 29 25 13 11 7 6

25 850 25 21 11 9 6 5

50 1550 20 17 9 7 5 4

75 2300 18 14 8 6 4 4

100 3000 16 13 7 6 4 3

125 3750 15 12 7 5 4 3

Table 3 reprinted with permission from the Canadian Entomologist, January 15, 2001.
181

Jack Pine Budworm

Choristoneura pinus

Freeman Lepidoptera: Tortricidae

Pendrel, B. A. 1985. Population distribution of Jack pine budworm--1984 described 

through pheromone trapping. Tech. Note 133. Canadian Forest Service, 

Maritimes Forest Research Centre; 4 p.

Objective

To determine if pheromone-baited traps are useful for monitoring C. pinus populations.

Abstract

The jack pine budworm, Choristoneura pinus Freeman, is an important pest of jack pine, Pinus banksiana

Lamb., and to a lesser extent 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.

The pattern of capture of C. pinus moths using pheromone-baited traps during 1984 resembled closely 

the expected distribution in both New Brunswick and Nova Scotia. The range of numbers caught and 

distribution suggest this trapping system may be an excellent tool for monitoring C. pinus populations. 

This system can be used to indicate where C. pinus populations are likely increasing, thus serving as an 

indication that defoliation may occur the following year.

Sampling Procedure

Place Pherocon II, or the larger capacity Pherecon 1C (Zoecon Corp., Palto Alto, CA), sticky traps baited 

with pheromone lures in the area of interest. Pheromone lures (90% 85/15 E/Z11-14:AC and 10% 

85/15 E/Z11-14:OH) in concentrations of 0.003, 0.03 or 0.3% in polyvinyl chloride (PVC) rods are 

suitable (Silk and others 1985). The author suggests that in its present state this system can be used to 

indicate where C. pinus populations are likely increasing, thus serving as an indication that defoliation may 

occur the following year.

Note

No information is provided regarding trap density or placement.

Reference

Silk, P. J.; Kuenen, L. P. S.; Tan, S. H.; Roelofs, W. L.; Saunders, C. J.; Alford, A. R. 1985. Identification of sex 

pheromone components of the jack pine budworm, Choristoneura pinus pinus Freeman. Journal of Chemical 

Ecology 7: 159-167.
182
PIERCING AND SUCKING INSECTS AND MITES

183

Hemlock Rust Mite

Nalepella tsugifolia Keifer 

Acari: Tetranychidae

Sidebottom, J. R. 1995. Rust mites in Christmas trees. Christmas Tree Notes. Raleigh, 

NC: N.C. Agricultural Extension Service, North Carolina State University; 2 p. 

(http://www.ces.ncsu.edu/nreos/forest/xmas/ctn_034.html)

Objective

To develop a scouting program for N. tsugifolia that aids in control decision-making for Christmas tree 

plantations.

Abstract 

The hemlock rust mite, Nalepella tsugifolia Keifer, is a frequent springtime problem on hemlock, Tsuga 

spp., eastern white pine, Pinus strobus L., and Fraser fir, Abies fraseri L., grown in the foothills of western 

North Carolina. Heavy feeding causes premature needle loss. Infested needles have a dusty, rust-colored 

appearance that reduces the aesthetic quality of ornamental and Christmas trees. 

A survey method was developed to determine if N. tsugifolia populations were high enough to warrant 

control measures. If 80% of all shoots sampled and at least eight mites are present on a single needle, 

then control measures were warranted. The action threshold can be modified depending on the grower’s 

costs and tree values.

Sampling Procedure 

To scout for N. tsugifolia, select 24-49 trees per hectare, concentrating on trees that were damaged the 

previous year. If no previous damage is evident, then choose trees at random. On white pine, sample 

needle cluster on the upper third of the tree in the southeast aspect where N. tsugifolia is found most 

often. Mites are usually concentrated near the follicle. In Fraser fir, examine shoots of current growth in 

the upper whorls and some from the lower ones. Scan both the upper and lower surfaces of the needles 

with a hand lens. If the buds have opened recently, then examine both the new and previous year’s 

growth.

Generally, if 80% of the shoots are infested and if there are at least eight mites present on a single shoot, 

then control is warranted.

Notes

Populations can increase quickly during favorable weather conditions. Therefore, scout weekly during 

critical periods in the spring. If the action threshold is not reached by early June, then sampling may be 

discontinued. Continue sampling for N. tsugifolia when activity resumes in the fall.
184

Spruce Spider Mite

Oligonychus ununguis (Jacobi) 

Acari: Tetranychidae

Sidebottom, J. R. 1995. The spruce spider mite in Fraser fir. Christmas Tree Notes. 

Raleigh, NC: N.C. Agricultural Extension Service, North Carolina State 

University; 4 p. 

(http://www.ces.ncsu.edu/nreos/forest/xmas/ctn_029.html)

Objective

To provide a sequential sampling system for estimating O. ununguis densities and timing insecticide 

applications before significant damage occurs.

Abstract

The spruce spider mite, Oligonychus ununguis (Jacobi), can be a significant pest of Fraser fir, Abies fraseri L., 

Christmas trees in western North Carolina particularly on windy ridges, southern exposures, and during 

periods of drought. Infested needles become yellow-spotted and webbed together after which time they 

turn brown and fall prematurely from infested trees. Growers often depend on pre-budbreak insecticide 

applications for control of the balsam woolly adelgid, Adelges picea (Ratzeburg), to also provide season 

long O. ununguis control. However, mite populations rebound quickly following spring applications and 

subsequent treatments may be warranted.

A detailed sequential sampling system for estimating O. ununguis populations and damage was developed. 

This method was derived from previous sampling methods developed by McGraw and Hain (1979). The 

economic thresholds for control decision-making were based on the percentage of trees that have mites, 

and varied depending on tree value and cost of control. If trees are greater than 1 m tall and 40% are 

infested, then control was warranted. As trees approach marketability, economic thresholds decreased 

accordingly. If threshold levels are not reached, the author indicates when scouting should resume based 

on a detailed set of criteria.

Sampling Procedure

The rigid block scouting method is used for sampling O. ununguis populations. Enter the block two to 

four rows from one corner and record that location on the scouting form so that on your next visit 

you will be able to initiate surveys one to two rows above or below this point. Walk the full length of 

the row, scanning from side to side up to five rows in each direction depending on tree size and visual 

obstructions. When you encounter an off-color, symptomatic tree go to that location to sample, but 

return to your original row to continue sampling. When you reach the end of the row, step over 6 to 10 

rows and continue this pattern until you have covered the entire block. 

Scout all Fraser fir stands from the year after planting through harvest to determine if mite numbers 

are great enough to cause damage. The number of times a field is scouted depends on tree size, mite 

prevalence, and prevailing weather conditions. For trees that do not receive a spring insecticide treatment 

for A. picea control, mite scouting should begin in mid-April. For trees that receive spring treatment, 

scouting efforts should begin in early June. Continue scouting until the first hard frost. 
185

To examine for O. ununguis, pull a single shoot of the most current growth from the suspect tree and 

look for the presence of mites, mite eggs, or mite damage with a magnifying lens. Sample the majority of 

shoots from the bottom 61 cm of the tree, but also check a few shoots near the top of each tree. Look at 

small shoots from inside the tree canopy since this is where O. ununguis is most often found. 

If any mites or eggs are found, then count the shoot as infested, and discontinue sampling as there is 

no need to count the number of mites or eggs. If no mites or eggs are found, count it as uninfested. 

Limit sampling to one shoot per tree. If you walk 15.5 m without seeing any trees that have symptomatic 

damage, pull a shoot from a tree at random and continue an additional 9 m before sampling another tree. 

Continue sampling at least one tree every 15-18 m and at least 37 shoots per hectare.

Calculate the percentage of infested shoots. The economic thresholds for O. ununguis are simply based 

on the percentage of trees that have mites, but vary depending on tree value and cost of control. The 

following thresholds are provided: 

 Size of the Tree Economic threshold (ET)

 1. Less than waist high 40% 

 2. Waist high to year before sale 20% 

 3. Year of sale 10%

If damage has not reached threshold levels, then scouting should resume according to the following 

criteria: 

1. If no mites or eggs are found and no damage is seen, return in 6 to 8 weeks.

2. If less than 10% of the shoots have mites or eggs, or if new O. ununguis damage has 

occurred since the last sample, return in 4-5 weeks. 

3. If greater than 10% of the shoots have mites or eggs, but it is less than the treatment 

threshold, return in 2 weeks.

4. If there are greater than 10 days of hot, dry weather, return in less than 2 weeks. Mite 

reproduction and life spans increase rapidly under these conditions.

5. If trees are marketable, scout at least once a month. 

Hot spot scouting can be used during hot, dry periods to determine how the weather is affecting mite 

activity in problematic areas. The hot spot becomes a representation of the rest of the block, and is 

identified through previous scouting methods. If mite activity is increasing in these hot spots, then go 

back and resurvey the entire block using the rigid block scouting method.

A sample scouting form for all Fraser fir pests is available through your local county extension office. 

For more information on scouting for mites, see the video, Detection and Control of the Spruce Spider 

Mite, which is available for the North Carolina Agricultural Extension Service. 

Reference

McGraw, J. R.; Hain, F. P. 1979. Spruce spider mite sampling system. Forest Res. Note. Raleigh, NC: N.C. 

Agricultural Extension Service, North Carolina State University; 6 p.
186

Eastern Spruce Gall Adelgid

Adelges abietis (L.) 

Homoptera: Adelgidae

Fidgen, J. G.; Teerling, C. R.; McKinnon, M. L. 1994. Intra- and inter-crown distribution 

of the eastern spruce gall adelgid, Adelges abietis (L.), on young white spruce. 

Canadian Entomologist 126: 1105-1110.

Objective

To determine both the intra- and inter-tree distribution of A. abietis on young white spruce, Picea glauca

(Moench.) Voss., that were open-grown or undergoing crown closure.

Abstract

The eastern spruce gall adelgid, Adelges abietis (L.), forms pineapple-shaped galls on the shoots of white 

and Norway, P. abies L., spruce. When populations are high, they cause shoot deformation and reduced 

growth. In most cases, the damage is negligible except in Christmas tree plantations or ornamental 

trees where aesthetic losses render trees unmarketable. Extensive sampling was conducted to determine 

the within and between tree distribution of A. abietis so that an efficient sampling scheme could be 

developed. The trees sampled in this study were 1-4 m in height and from 9-15 years old.

Adelgid galls were found mainly on lateral shoots within the mid-crown of open grown trees. After 

crown closure, most galls were found in the upper crown, above the point where branches of adjacent 

trees overlapped. The inter-tree distribution of A. abietis did not differ significantly from the negative 

binomial distribution, indicating a high degree of aggregation among trees. Therefore, a stratified random 

sampling plan using the first 20 lateral shoots of an open grown, mid-crown branch was recommended 

for monitoring A. abietis populations.

Sampling Procedure

Select every fifth to tenth tree in every fifth to tenth row systematically in the area of concern, and 

inspect mid-crown branches for presence of A. abietis. Because A. abietis populations are usually clumped, 

a mid-crown branch of each adjacent tree should also be assessed (i.e., eight neighboring trees adjacent to 

the sample tree). On each tree, select either a west- or south-facing open-grown, mid-crown branch and 

count the number of A. abietis galls in the first 20 lateral shoots examined. Express A. abietis damage as 

the percentage of lateral shoots infested per tree. 

Note

The sample unit was designed primarily for use in Christmas tree plantations or forests less than 15 years 

old. 
187

Cooley Spruce Gall Adelgid

Adelges cooleyi (Gillette) 

Homoptera: Adelgidae

Lasota, J. A.; Shetlar, D. J. 1986. Assessing seasonal and spatial abundance of Adelges 

cooleyi (Gillette) (Homoptera: Adelgidae) by various sampling techniques. 

Environmental Entomology 15: 254-257.

Objective

To evaluate four sampling methods for assessing seasonal and spatial distributions of A. cooleyi on 

Douglas-fir, Pseudotsuga menziesii (Mirb.) Franco.

Abstract

The Cooley spruce gall adelgid, Adelges cooleyi (Gillette), is serious pest in nurseries, Christmas tree 

plantations, and forests throughout northern North America. The primary host is spruce, Picea spp., 

although winged adults fly to Douglas-fir which is the alternate host. Feeding causes distortion, spotting, 

and premature needle abscission. Four sampling methods (1-min, 3-min, 5-branch, and 15-branch) were 

evaluated for accuracy in estimating A. cooleyi populations on Douglas-fir in Pennsylvania.

The 3-min counts produced the greatest number of A. cooleyi while the 5-branch counts produced the 

least. Comparisons between the 1-min and 15-branch counts differed significantly on only one sample 

date. Early in the season, A. cooleyi populations were high and thus all four sampling techniques produced 

similar accuracy. However, as populations declined, the larger sample units (3-min and 15-branch 

counts) were generally more accurate at estimating A. cooleyi densities than the smaller ones. The most 

representative area to sample was in the upper half of the crown regardless of the time of year. The 

1-min count was most useful to commercial growers or others that have little time to sample. 

Sampling Procedure

Sample 1 to 2 shoots from the upper half of the live crown from each of 10-20 Douglas-fir 1.5-1.8 m 

tall. Northern and eastern aspects should give the most representative sample regardless of the time 

of year. Count all A. cooleyi found on the current year’s growth for 1-min (time constrained sampler) 

or 3-min (detailed sampler). Conduct sampling procedures three times annually to assess changes in 

population density.

Note

This method also applies to forested and urban areas.
188

Balsam Woolly Adelgid

Adelges piceae (Ratzeburg) 

Homoptera: Adelgidae

Amman, G. D. 1969. A method of sampling the balsam woolly aphid on Fraser fir in 

North Carolina. Canadian Entomologist 101: 883-889.

Objective

To develop a method of sampling adelgid populations, which would provide a means of assessing 

mortality factors and population density.

Abstract

The balsam woolly adelgid, Adelges piceae (Ratzeburg), is an introduced species first recorded in North 

America in Maine in 1908. It has since spread throughout the native range of balsam, Abies balsamea (L.) 

Mill., and Fraser, A. fraseri (Pursh) Poir., fir and is also found in the Pacific Northwest. Mortality occurs 

quickly in trees suffering from extensive stem attacks, which appear as white woolly masses. A study 

was conducted on Mt. Mitchell, North Carolina in a dense stand of Fraser fir with dominants and codominants 7.6-10.7 m tall. Populations of A. picea were sampled without replacement.

A sample of 2 to 16 pieces of bark (depending on the observed density), 1.27 cm in diameter from the 

lower bole of each of 10 trees yielded a standard error of ±10% of the mean. An acceptable level of 

precision was obtained by taking 77 samples 1.4-1.7 m and 71 samples 0.8-1 m along the bole (n = 148 

samples). During winter and from mid-July through mid-August, sampling intensity could be reduced to 

40 pieces of bark.

Sampling Procedure

Select randomly 10 trees of similar diameter. Take eight pieces of bark with a 1.27 cm diameter cork 

punch from at least two different cardinal directions at 0.8-1 and 1.4-1.7 m along the bole of each tree. 

Once the bark is cut, use a knife to remove (pry) the bark until the cambium layer is reached. This 

procedure will minimize pitch flow. Store samples in vials, and count and record the number of A. picea

under a dissecting microscope in the laboratory. 
189

Pine Leaf Adelgid

Pineus pinifolia (Fitch) 

Homoptera: Adelgidae

Dimond, J. B. 1974. Sequential surveys for the pine leaf chermid, Pineus pinifoliae. Tech. 

Bull. 68. Orono, ME: University of Maine, Agricultural Experiment Station; 15 p.

Objective

To develop field procedures for the classification of white pine damage produced by P. pinifoliae, and to 

relate infestation levels to tree damage. 

Abstract

The range of the pine leaf adelgid, Pineus pinifoliae (Fitch), coincides with that of red, Picea rubens Sarg., 

and black, P. mariana (Mill.), spruce, its primary hosts, wherever they grow in proximity to eastern white 

pine, Pinus strobus L., its alternate host. Infestations on spruce result in terminal, compact galls that have 

the appearance of true cones consisting of many chambers each containing a single adelgid. Populations 

on white pine cause growth reduction and tree mortality in cases of extreme infestation.

A sequential survey procedure was described for determining infestation levels of P. pinifoliae as well as 

for classifying damage to white pine. Damage classes are based on the degree of needle stunting and 

needle color. Sampling of white pine was conducted until late-June. Galls on red and black spruce were 

sampled beginning around mid-May when galls can be differentiated easily from uninfested buds.

Sampling Procedure

Table 1 shows the degree of damage caused by P. pinifoliae at several life stages and population densities. 

There is one method to estimate P. pinifoliae damage and two methods to estimate population levels.

Estimating Damage

Needle length: Remove one branch from the mid-crown of 20 white pine trees, and remove 

a twig from the mid-portion of the branch. Remove 10 current-year fascicles from the midportion of the twig and record needle length. Record the reduction in needle length for all 

fascicles less than 70 mm in length (i.e., if fascicle is 58 mm, then record 12). Add the total 

length of needle reductions for all 20 fascicles, reference the sequential sampling plan (Table 2) 

for a two twig sample, and continue sampling until a decision is met. Damage will be classified 

as either tolerable, critical, or intolerable based on the cumulative stunting of needles (Table 2). 

Sampling should be conducted when needle elongation is complete (i.e., end of September in 

this study). 

Estimating Population Levels

Sampling the gallicola migrans stage: Sample this stage on white pine during June, or after the 

gallicolae have settled. Collect twigs as above, sampling one year old internodes, and counting 
190

and recording them as infested or uninfested. Continue sampling twigs from the same branch 

until 100 fascicles have been counted and then calculate the percentage of fascicles infested by P. 

pinifoliae. Reference the sequential sampling plan (Table 3) and continue sampling until a decision 

is met. Populations will be classified as either tolerable, critical, or intolerable based on the 

cumulative percentage of infested fascicles (Table 3). 

Sampling for galls on red and black spruce in mixed stands: Sample around mid-May when 

galls can be differentiated easily from uninfested buds. Red and black spruce should be sampled 

in proportion to their relative abundance in the stand. Remove one branch from the second 

crown quarter from the top of the tree. Count and record the number of galls. Reference the 

sequential sampling plan (Tables 4), and continue sampling until a decision is met. Damage will 

be classified as either tolerable, critical, or intolerable based on the cumulative number of galls 

per branch (Tables 4). 

Notes

A working knowledge of the complex biology of P. pinifoliae is required to successfully implement these 

sampling methods (Balch and Underwood 1950). Make sure not to sample edge trees where within-tree 

distribution of P. pinifoliae may not be uniform as in the infested stand.

Reference

Balch, R. E.; Underwood, G. R. 1950. The life history of Pineus pinifoliae (Fitch) (Homoptera: Adelgidae) and its 

effect on white pine. Canadian Entomologist 82: 117-123.

Tables 

Table 1. Classification of degrees of damage produced by the pine leaf chemid on white pine and 

population levels of several life stages that produce those degrees of damage.

Damage

category

Damage description No. of neosistens per cm 

of shoot

No. of 

gallicolae/10 

fascicles on 

pine shoots

% of 

fascicles 

infested by 

gallicolae

Galls per 

branch; 

stands 

mostly pine

Galls per 

branch; 

stands mostly spruce

Tolerable Needles normal 

length, >70 mm, or 

slightly stunted, up to 

5mm

<5 <0.06 <5.5 <5 <1

Critical Needles moderately 

stunted, 15-25 mm; 

(needle length 45-55 

mm), some schools 

chlorotic or dead

12-38 1.7-5.5 10.4-30.1 13-20 3-7

Intolerable Needles heavily 

stunted, >35 mm; 

(needle length <35 

mm), many shoots 

chlorotic or dead

>165 >25 >65 >28 >10
191

Table 2. Sequential table for classifying pine leaf chemid damage on white pine, using needle stunting as 

the criterion. Calculated at 90% confidence level, using equations for a normal distribution. 

No. of twigs 

examined (n)

Cumulative stunting (<70 mm) in mm

Tolerable-d1 Critical-d2 Critical-d3 Intolerable-d4

1 ---

CONTINUE SAMPLING

--- ---

CONTINUE SAMPLING

43

2 7 33 47 73

3 17 43 77 103

4 27 53 107 133

5 37 63 137 163

6 47 73 167 193

7 57 83 197 223

8 67 93 227 253

9 77 103 257 283

10 87 113 287 313

11 97 123 317 343

12 107 133 347 373

13 117 143 377 403

14 127 153 407 433

15 137 163 437 463

16 147 173 467 493

17 157 183 497 523

18 167 193 527 553

19 177 203 557 583

20 187 213 587 613

d1 - 10n-13.18 d2

 = 10n+13.18 d4 = 30n+13.18
192

Table 3. Sequential table for classifying damage potential of the gallicolae migrans stage of the pine leaf 

chemid on pine. Calculated at 90% confidence level, using equations for a binomial distribution.

No. of 

twigs 

examined 

(n)

Cumulative % infested fascicles

Tolerable-d1 Critcal-d2 Critical-d3 Intolerable-d4

1 ---

CONTINUE SAMPLING

--- ---

CONTINUE SAMPLING

---

2 --- --- --- ---

3 --- --- --- 249

4 7 52 52 292

5 15 59 95 335

6 22 66 138 378

7 30 74 181 421

8 37 81 224 464

9 44 89 267 507

10 52 96 310 550

11 59 103 353 593

12 66 111 396 636

13 74 118 439 679

14 81 126 482 722

15 89 133 525 765

16 86 140 568 808

17 104 148 611 851

18 111 155 654 894

19 118 163 697 937

20 126 170 740 980

d1

 = 7.39n-22.06 d2 = 7.39n+22.06 d3 = 43n-119.9 d4 = 43n+119.9
193

Tables reprinted with permission from the University of Maine, Orono, Janaury 15, 2001.

Table 4. Sequential table for classifying damage potential of galls produced by the pine leaf chermid 

on spruce, where pine is a greater component of the stand than spruce. Calculated at 90% confidence 

level for tolerable vs. critical and at 70% level for critical vs. intolerable, using equations for a negative 

binomial distribution. common k=0.901. 

No. of twigs 

examined (n)

Cumulative number of galls per branch

Tolerable-d1 Critical-d2 Critical-d3 Intolerable-d4

1 ---

CONTINUE SAMPLING

--- ---

CONTINUE SAMPLING

95

2 --- --- --- 119

3 1 --- --- 144

4 9 --- --- 168

5 17 --- --- 193

6 25 69 75 217

7 32 77 100 241

8 40 85 124 265

9 48 93 148 290

10 56 100 173 314

11 64 108 197 339

12 71 116 221 363

13 79 124 246 387

14 87 131 270 412

15 95 139 294 436

20 134 178 416 558

25 173 217 538 680

30 212 256 660 801

35 251 295 782 923

40 290 335 903 1045

45 329 374 1025 1167

50 368 413 1147 1289

d1 = 7.81n-22.21 d2 = 7.81n+22.21 d3 = 24.3n-70.8 d4 = 24.3n+70.8
194

Painted Maple Aphid

Drepanaphis acerifoliae (Thomas) 

Homoptera: Aphididae

Dreistadt, S. H.; Flint, M. L. 1995. Landscape pest monitoring methods and training 

managers to use them. Journal of Arboriculture 21: 1-6.

Objective

To describe a monitoring method for D. acerifoliae useful to urban foresters in control decision-making.

Abstract

The painted maple aphid, Drepanaphis acerifoliae (Thomas), is a common pest of maples, Acer spp., 

growing in urban areas. Infestations result in dieback and aesthetic damage. Honeydew, a waste product 

excreted from the posterior of the insect, drips from infested leaves and is often considered a nuisance. 

Aphid populations can be monitored effectively in urban environments by using a type of water 

sensitive, yellow paper that turns blue when in contact with honeydew droplets (Dreistadt 1987). This 

monitoring technique was described in relation to controlling the painted maple aphid on silver maple, 

Acer saccharinum L., in California. Control measures were warranted whenever honeydew drop densities 

exceeded 1-2 drops/cm2

/4 h. 

Sampling Procedure

To monitor D. acerifoliae populations, a water sensitive, yellow card is used to detect the presence of 

aphids. These cards produce a dark blue dot whenever honeydew lands on the surface. Tape each card to 

a piece of cardboard, which is attached to a bent wire coat hanger. Hang each card 46 cm beneath lower 

crown foliage. Place a card in each cardinal direction weekly. Deploy cards for 4 h, retrieve, and determine 

the number of dots per square centimeter. Control is warranted if densities exceed 1-2 drop/cm2

 on any 

one card.

Note

Aphid populations are distributed normally and occur in urban environments where honeydew 

production is undesirable. 

Reference

Dreistadt, S.H. 1987. Monitoring honeydew excretion in the field as a method of sampling Illinoia liriodendri 

(Homoptera: Aphididae) infesting Liriodendron tulipiferia. Journal of Economic Entomology 80: 380-383.
195

Balsam Twig Aphid

Mindarus abietinus Koch 

Homoptera: Aphididae

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.

Objective

To compare methods for determining infestation levels of M. abietinus in Christmas tree plantations.

Abstract

The balsam twig aphid, Mindarus abietinus Koch, causes distortion and loss of needles on balsam fir, Abies 

balsamea (L.), Christmas trees. This study, conducted in balsam fir Christmas tree plantations of central 

Wisconsin, compared two sampling methods (beating discs and visual counts of infested shoots) to 

monitor aphid densities and make informed decisions regarding control.

The beat disc method was most effective at detecting M. abietinus before and during budbreak, while the 

visual count method was most effective after budbreak. A minimum of 15 trees in each stand was the 

recommended sample size for both methods. For monitoring purposes, growers should sample before 

budbreak by using the beat disc method, and limit insecticide applications to trees with greater than two 

fundatrices per sample.

Sampling Procedure

Beat disc method: Use a 53.4-cm2

 circular beating disc for sampling aphid numbers. Each disc consists 

of a piece of black velvet glued to the inside of a 17 cm diameter plastic embroidery ring. Place the 

ring within the outer mid-crown of each tree and beat the foliage five times with a gloved hand to 

dislodge insects onto the disc. Aspirate all aphids and place in vials containing 70% ethyl alcohol for 

later identification and tally. Conduct the sample at or before budbreak (i.e., mid-May in this study). Beat 

samples should be processed immediately to ensure proper timing of control measures in the current 

year.

Visual count method: Count the number of infested and uninfested shoots on a 20 cm long mid-crown 

branch on each of 15 trees previously unsampled. Calculate the proportion of infested shoots. The 

visual count sample should coincide with the peak of wingless, spring females (i.e., late May), winged 

adults (i.e., early June), and after egg laying (oviposition) has occurred (i.e., early July). On the last sample 

date, count and record the number of shoots with no curling, slight curling (needles slightly twisted) or 

extensive curling (permanent). 

After shoots emerge and become infested, visual counts of infested shoots are reliable indicators of 

future damage. For current year control decision-making, growers should monitor aphid populations by 

the beat disc method and apply registered insecticides when high infestation levels occur (i.e., greater than 

two or three fundatrices per sampling disc).
196

Saratoga Spittlebug

Aphrophora saratogensis (Fitch) 

Homoptera: Cercopidae

Wilson, L. F. 1987. Saratoga spittlebug—its ecology and management. Agric. Handb. 

No. 657. Washington, DC: U.S. Department of Agriculture, Forest Service; 56 p.

Objective

To provide survey procedures useful for assessing spittlebug populations and predicting population 

trends.

Abstract

The Saratoga spittlebug, Aphrophora saratogensis (Fitch), is the most destructive sap-sucking forest pest of 

pines, Pinus spp., in eastern North America. Nymphs feed on alternative hosts, typically sweetfern, until 

mid-summer and adults feed on pines until autumn. The most obvious signs of infestation are spittlelike masses on alternate hosts, reddish-colored branches on host trees, and tan or brown flecks on the 

outer bark. Heavy infestations cause top kill, stem deformity, growth loss, and tree mortality. The greatest 

impact occurs in young plantations of red, Pinus resinosa Ait., and jack, Pinus banksiana Lamb., pine in the 

Lake States and Canada.

Two types of surveys are available to assess changes in A. saratogensis density and to predict stand risk 

or hazard. If a feeding scar survey indicated that the average number of scars was less than 25, then 

no further surveys were warranted. If the survey value was 20-25, then a feeding scar survey should be 

conducted the following fall. If the survey value was greater than 25, then a nymph survey should be 

conducted the following spring. A density of one nymph per tree-unit indicated a 25% reduction in tree 

growth. In areas with greater than one nymph per tree and with visible flagging or defoliation, control 

was recommended for the same year. A risk rating system was developed for A. saratogensis.

Sampling Procedure

You may stop the survey after any step if feeding injury is seen or the insect is collected and verified. 

When infestations are too low to show injury, you may need to sample several trees or alternate hosts 

before locating A. saratogensis.

Feeding scar survey: This survey estimates the severity of adult feeding which predicts whether a more 

detailed nymphal survey is warranted the following spring. Sample areas of moderate to high risk in 

autumn based on presence of alternate hosts. Determine the number of samples you will take according 

to the area at risk: <4.5 ha (20 samples), 4.5-8.1 ha (25 samples), 8.1-20.2 ha (30 samples), and >20.2 

ha (>35 samples). Conduct a survey systematically at specified intervals in order to get adequate stand 

coverage. At each sample point, select a tree of average height and remove a 10-cm section of two-yearold growth from an upper whorl. Remove the bark with a knife and record the number of scars (red 

flecks) on the sapwood and then average the number of scars from the samples. If the value is less than 

25, no further surveys are required. If the average is between 20-25, the stand should be scar surveyed 

again the following fall. If the average is greater than 25, the stand should be surveyed for nymphs in the 

spring.
197

Nymph survey: The survey of A. saratogensis nymphs determines the current threat of injury. Begin 

looking for nymphs in spittlemasses the second week of June and tally when most appear to be late 

instars (third to fifth) which more accurately reflect the adult population and subsequent damage. The 

first to fourth instars are black and red whereas fifth instars are chestnut brown. If a current lack of 

spittle-masses does not warrant concern, no further sampling needs to be done that year. However, 

surveys should be scheduled periodically until crown closure occurs.

Select the number of 400-m2

 (0.1 acre) plots to cover the area in question: 0.4-2 ha (1 plot), 2.4-4 ha (2 

plots), 4.5-8.1 ha (3 plots), 8.5-16.2 ha (4 plots) and >16.2 ha (5 plots). Determine the number of trees 

in each plot, average number of whorls per tree, and height of 10 trees scattered throughout the plot. 

Calculate and record the tree-units for the plot (A) by multiplying the number of trees by the average 

number of whorls by the average tree height. Count the number of nymphs in a plot 63.5 by 63.5 cm 

using a sampling frame to delineate plot boundaries. When you find one nymph, stop sampling and 

record that sample as a (+). If no nymphs are found after examining all host plants record the sample 

as a (-). After taking all 50 samples, count the (+) and multiply by 2 to determine the percentage of 

samples infested with nymphs, which provides an estimate of the number of nymphs per 400-m2

 plot 

(B). Calculate the number of nymphs per tree-unit (C) by taking the number of nymphs per plot (B) 

and dividing by the number of tree-units per plot (A). This value (C) is used to predict probable damage 

(Table 12) and the need for future surveys and control (Table 13).

Tables

Table 12. Damage level categories for adult spittlebug feeding.

Damage level Nymph/tree fruit Potential growth reduction 

 (%)

Very low - lateral terminal growth 

differences

0.25 2

Low - up to 4 yr of growth 

reduction

0.50 6

Moderate - up to 10 yr of growth 

reduction, scattered flagging, 

some degradation 1.00 25

High - whold branch flagging, 

dead tops, extensive degradation, 

some dead trees 2.00 41

Very high - dead tops, extensive 

degradation, many dead trees

6.00 66
198

Table 13. Key to action recommended after nymphal appraisal survery1

0a. Nymphs/tree-unit less than 1.0 - see no. 1

0b. Nymphs/tree-unit 1.0 or more - see no. 8

1a. Trees shorter than 10 ft - see no. 2

1b. Trees 10 ft or taller - see no. 4

2a. Nymphs/tree-unit less than 0.15 - evaluate again in 3 years

2b. Nymphs/tree-unit 0.15 or more - see no. 3

3a. Nymphs/tree-unit more than 0.25 - evaluate next year

3b. Nymphs/tree-unit 0.15 to 0.25 - evaluate in 2 years

4a. Trees from 10 to 12 ft - see no. 5

4b. Trees taller than 12 ft - see no. 7

5a. Nymphs/tree-unit more than 0.15 - see no. 6

5b. Nymphs/tree-unit 0.15 or less - no need to reevaluate

6a. Nymphs/tree-unit more than 0.25 - evaluate next year

6b. Nymphs/tree-unit 0.15 to 0.25 - evaluate in 2 years

7a. Nymphs/tree-unit more than 0.40 - reevaluate next year

7b. Nymphs/tree-unit 0.40 or less - no need to reevaluate

8a. Nymphs/tree-unit 1.0 to 2.0 - if there is flagging or noticeable degradation, contol 

this year, if not, reevaluate next year

8b. Nymphs/tree-unit more than 2.0 - control this year
199

Citricola Scale

Coccus pseudomagnoliarum (Kuwana) 

Homoptera: Coccidae

Dreistadt, S. H.; Flint, M. L. 1995. Landscape pest monitoring methods and training 

managers to use them. Journal of Arboriculture 21: 1-6.

Objective

To describe a method of monitoring C. pseudomagnoliarum populations to determine when control 

measures should be applied.

Abstract

The citricola scale, Coccus pseudomagnoliarum (Kuwana), often infests hackberry, Celtis spp., in urban 

plantings in California. Infestations cause dieback and a proliferation of honeydew, a waste product that 

forms a sticky residue on anything below infested trees. Populations can be monitored effectively in 

urban landscapes with sticky tape traps designed to sample the crawler stage. If control treatments are 

warranted, insecticides or oils are applied after peak crawler emergence or after a sharp increase in the 

number of crawlers caught per trap (i.e., 40 crawlers per traps). 

Sampling Procedure

Monitor C. pseudomagnoliarum crawlers using double-sided transparent tape available in any stationary 

store. To make traps, a twig or small branch (9-13 mm in length) is wrapped tightly with a strip of tape 

about 12 cm in length. Double over the free end of each sticky band to make a handle to facilitate tape 

removal. Newly-hatched crawlers will get stuck in the tape traps as they search for new feeding sites.

Deploy two or three traps per tree before crawlers are expected (i.e., late April in this study). Once 

deployed, monitor and change traps with new material weekly in the same location. Count the number of 

crawlers from each monitoring date to determine peak crawler emergence. Apply treatment during peak 

crawler emergence or when a sharp increase in crawler number is noticed in the traps, or both.

Notes

The reader must be able to identify C. pseudomagnoliarum life stages. This technique is very efficient and 

took one person 1 h per week during spring to collect and replace 22 traps.
200
SEED AND CONE INSECTS

201

Spruce Cone Maggot

Strobilomyia neanthracina Michelsen 

Diptera: Anthomyiidae

Sweeney, J. D.; Miller, G. E.; Ruth, D. S. 1990. Sampling seed and cone insects in spruce. 

In: West, R. J., editor. Proceedings of the cone and seed pest workshop. 1989 

October; St. John’s, Newfoundland. Inf. Rep. N-X-274. Canadian Forest Service; 

63-75.

Objectives

To determine if the percentage of damaged seeds per cone is positively related to S. neanthracina density 

per conelet; to determine what sample size is necessary to estimate S. neanthracina egg density; and to 

determine what infestation levels are required to justify control.

Abstract

The spruce cone maggot, Strobilomyia neanthracina Michelsen, is a destructive, seed-eating cone fly 

of spruce, Picea spp., in Canada. One egg is laid per cone and after hatching the larva feeds on the 

developing seeds while spinning around the cone axis. A study was conducted in the interior of British 

Columbia, Canada, to determine if seed damage to white, Picea glauca (Moench) Voss., and Engelmann, 

P. engelmannii L., spruce was positively related to S. neanthracina infestation level and density per conelet. 

These data were used to develop recommendations for sampling S. neanthracina to determine if control 

measures were warranted. The percentage of seeds damaged per cone was positively related to both 

the percentage of cones infested and density of S. neanthracina per cone. The number of sample trees 

required to estimate egg density with 90% confidence and 10% error was from 218 to 542. This sampling 

intensity was considered too large to be practical for field applications. The optimal number of conelets 

to sample per tree was two. Control measures were warranted if S. neanthracina egg densities exceeded 0.3 

per conelet.

Sampling Procedure

Systematically select from 218 to 542 trees to be sampled in the area of concern. At each tree, collect two 

conelets from the upper to mid-crown when conelets are about half pendant. Conelets can be bulked 

and stored at -10 oC until dissected. The number of person-days required to sample this many conelets 

ranged from 5 to 12.

Dissect conelets with a pair of fine forceps under a stereoscopic microscope at 10 power magnification. 

Starting from the base of each cone and working towards the tip, pull each cone scale away from the 

conelet, looking for presence of eggs, larvae, and feeding damage. Eggs are white and oblongate (about 

1.4 by 0.5 mm) and are laid between the cone scales. Immature larvae are more difficult to see if they 

have just hatched from the egg. Look for hatched, flattened eggs with signs of feeding nearby and the 

small white translucent larva with its pair of black mouth hooks.
202

Spruce Cone Maggot

Strobilomyia neanthracina Michelsen 

Diptera: Anthomyiidae

Sweeney, J. D. 1998. Sequential sampling of spruce conelets to predict the category of 

seed loss due to spruce cone maggots. Unpublished Report, Canadian Forest 

Service, Natural Resources Canada, P.O. Box 4000, Fredericton, NB. Canada, 

E3B 5P7.

Objective

To develop a method of predicting seed orchard seed losses from S. neanthracina before significant losses 

occur. This method indicates when control measures are needed.

Abstract

The spruce cone maggot, Strobilomyia neanthracina Michelsen, is a destructive, seed-eating cone fly of 

spruce, Picea spp., in eastern Canada. Usually, one egg is laid per cone. After hatching, the larva feeds on 

the developing seeds while spinning around the cone axis.

A method of determining the need for control of S. neanthracina in white, Picea glauca (Moench) Voss., 

or Engelmann, P. engelmannii L., spruce seed orchards was presented. The optimum sampling intensity 

for S. neanthracina was one conelet from each of 100 trees when conelets on most trees were about half 

pendant. Infestations were classified as light (≤8%), moderate (12-35%), or heavy (≥40% of conelets 

infested).

Sampling Procedure

Collect one conelet from each of 100 trees when conelets on most trees are about half pendant. Select 

trees systematically to ensure adequate coverage across the entire block or orchard being sampled. Bulk 

all conelets into one sample. Process samples as soon as possible.

First, dissect 20 conelets with a pair of fine forceps under a stereoscopic microscope at 10 power 

magnification. Starting at the base of each cone and work towards the tip, pulling each cone scale away 

from the conelet while searching for the presence of eggs, larvae, and feeding damage. Eggs are white 

and oblong (about 1.4 by 0.5 mm) and are laid between the cone scales. Immature larvae are more 

difficult to see if they have just hatched from the egg. Look for hatched, flattened eggs with signs of 

feeding nearby and the small white translucent larva with its pair of black mouth hooks. Once a cone 

maggot has been found, record the conelet as infested. Compare the number of infested conelets in 

your first 20 conelets dissected with the numbers in Table 1. If you find 7 or more infested cones out 

of 20, then at least 12% loss of filled seed is predicted. If 15 or more infested cones are found, then 

40% loss of filled seed is predicted. If fewer than 9 of 20 cones are infested, dissect another 10 cones 

and reference Table 1 again. Continue dissecting conelets in bunches of 10 until the cumulative number 

of infested conelets per number of conelets dissected corresponds to ≤8%, 12-35% or ≥40%. Dissect 

a maximum of 100 cones. If the cumulative number of infested conelets does not fall into one of the 

damage classes, you can predict crudely the percentage of seed loss as follows:

% seed loss = % infested conelets x 0.69
203

Note

This sampling plan requires up to 1 day to complete, depending on the level of infestation.

Table

Table 1. Sampling plan for predeciting the percentage of seeds lost to S. neanthracina feeding on P. glauca.

Cumulative no. cones with eggs or larvae

No. cones 

dissected Seed loss ≤8% 12-35% ≥40%

20 -

Continue sampling

7

Continue sampling

≥ 18

30 0 8 - 12 ≥ 25

40 ≤ 2 10 - 18 ≥ 31

50 ≤ 3 11 - 25 ≥ 37

60 ≤ 5 13 - 31 ≥ 43

70 ≤ 6 14 - 37 ≥ 49

80 ≤ 8 16 - 43 ≥ 55

90 ≤ 9 17 - 49 ≥ 61

100 ≤ 11 19 - 55 ≥ 68

Table 1 reproduced with permission from Natural Resources Canada, Canadian Forest Service, 

copyright January 15, 2001, Government of Canada.
204

Douglas-Fir Cone Gall Midge

Contarinia oregonensis Foote 

Diptera: Cecidomyiidae

Miller, G. E. 1986. 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.

Objectives

To examine the distributions of C. oregonensis eggs within and among trees, and to develop an egg 

population sampling technique.

Abstract

The Douglas-fir cone gall midge, Contarinia oregonensis Foote, is a serious pest of Douglas-fir, Pseudotsugae 

menziesii (Mirb.) Franco, seed in forests and seed orchards of western North America. Efficient sampling 

procedures for estimating egg densities of C. oregonensis were developed from data collected in Douglas-fir 

seed orchards on Vancouver Island, Canada, 1978-1981.

Aspect, conelet density per branch, conelet position from branch tip, or conelet length and color did not 

influence the oviposition preferences of C. oregonensis. However, egg density was positively correlated with 

the total number of conelet scales in all orchard-years (P < 0.05). The optimum sampling pattern was to 

sample one conelet on 120 trees from the mid-point of the cone-bearing portion of the crown. Sampling 

required a processing time of up to 120 h, depending on the egg densities encountered. 

Sampling Procedure

Collect one conelet from each of 120 trees to provide an estimate of the average number of eggs per 

conelet with a standard error of 10% and a confidence of 90%. Dissect, count, and record the number 

of eggs. The following recommended sample sizes are also provided and adjusted for crop size:

Number of producing trees 50 100 200 500 1000

Number of sample trees 18 40 76 80 93 

In Douglas-fir seed orchards in British Columbia, normally ≤90 samples are required involving a sampling 

time of up to 90 h when egg densities are high, because of the limited number of cone-producing trees 

in any one year. Conelets can be stored at 0o

C for 2-3 months and retain their suitability for egg counts.

Notes

The effectiveness of this technique in non-orchard situations is unknown. Consult our review of Miller 

(1986) for more details concerning damage predictions resulting from C. oregonensis infestations.

Reference

* Miller, G. E. 1986. Damage prediction for Contarinia oregonensis Foote (Diptera: Cecidomyiidae) in Douglas-fir seed 

orchards. Canadian Entomologist 118: 1297-1306.
205

Douglas-Fir Cone Gall Midge 

Contarinia oregonensis Foote 

Diptera: Cecidomyiidae

Miller, G. E. 1986. Damage prediction for Contarinia oregonensis Foote (Diptera: 

Cecidomyiidae) in Douglas-fir seed orchards. Canadian Entomologist 118: 1297-

1306.

Objective

To develop a sequential sampling plan for classifying infestations based on the relationship between egginfested conelets in the spring and the number of damaged seeds per cone at harvest.

Abstract

The Douglas-fir cone gall midge, Contarinia oregonensis Foote, is a serious pest of Douglas-fir, Pseudotsugae 

menziesii (Mirb.) Franco, seed in forests and seed orchards of western North America.

Damage by C. oregonensis was correlated positively with the number of egg-infested scales per conelet in 

the spring. Two methods that determine the number of samples required to estimate populations of C. 

oregonensis accurately are presented. The optimum sample size for estimating densities of egg-infested 

scales in seed orchards was one conelet per tree from each of 154 trees per orchard. Cones were sampled 

halfway up the cone-bearing portion of the crown. A sequential sampling plan relative to a critical density 

was also developed for control decision-making. An average of 2.6 egg-infested scales per conelet was 

determined to cause 10% seed loss assuming 85% insecticide efficacy. If the average number of egginfested scales ≥2.6, then control was warranted.

Sampling Procedure

Fixed sample size plan: The optimal number of conelets per tree is determined using procedures 

presented in the original publication (Table 2). Sample one conelet from each of 154 trees (90% 

confidence and 10% error), which should take approximately 17.5 h.

Sequential sampling plan:

Individual trees: Sample cones halfway up the cone-bearing portion of the crown. Dissect, 

count, and record the number of infested scales, referencing the sequential sampling plan (Fig. 

4). Continue sampling until a decision is met or 97 cones are sampled. If the cumulative total 

number of egg-infested scales drops below the lower line, control is not warranted. If the 

cumulative total of egg-infested scales falls above the upper line, control is warranted.

Seed orchards: Sample one conelet per tree halfway up the cone-bearing portion of the crown. 

Dissect, count and record the number of infested scales, and reference the sequential sampling 

plan (Fig. 5). The maximum number of trees to be sampled is 154.

This sequential sampling plan has been used operationally in British Columbia since 1981. 

During this period most orchards were sampled and decisions reached by the time 100 conelets 

had been processed with an average processing time of 7-8 h. 
206Notes A larger than expected sampling error may be realized. This technique was developed in seed orchards and may require modification for clonal 

orchards if variation to C. oregonensis susceptibility exists among cones.
207

Fig. 4. Sequential sampling graph for individual trees with 10% sampling error and 90% confidence 

using a critical density equivalent to 10% seed loss. Conelets should be collected from the midpoint of 

the conelet-bearing region.
208

Table and figures reprinted with permission from the Canadian Entomologist, January 15, 2001.

Fig. 5. Sequential sampling graph for an orchard taking one conelet per tree, at the midpoint of 

the conelet bearing reagion, with 10% sampling error and 90% confidence using a critical density 

equivalent to 10% seed loss (2.6 egg-infested scales per conelet).
209

Douglas-Fir Cone Moth

Barbara colfaxiana (Kearfott) 

Lepidoptera: Tortricidae

Sweeney, J. D.; Miller, G. F. 1989. Distribution of Barbara colfaxiana (Lepidoptera: 

Tortricidae) eggs within and among Douglas-fir crowns and methods for 

estimating egg densities. Canadian Entomologist 121: 569-578.

Objective

To develop a sequential sampling plan that predicts when B. colfaxiana populations are high enough to 

cause a 10% loss of Douglas-fir seed.

Abstract

The Douglas-fir cone moth, Barbara colfaxiana (Kearfott), is the most prevalent pest of Douglas-fir, 

Pseudotsugae menziesii (Mirb.) Franco, seed in British Columbia. The spatial frequency distributions of B. 

colfaxiana eggs in Douglas-fir trees and stands were determined by dissecting 13,262 conelets collected 

from 81 trees at three sites in 2 years.

Sampling Procedure

Collect Douglas-fir conelets after they have begun to turn down, following pollination and the 

oviposition period of B. colfaxiana, but before they become pendant. Sample at least 10 trees 4-14 m tall, 

collecting three conelets per tree. Consult the sequential sampling table (Fig. 2), and continue sampling 

until a decision is met. If the cumulative number of eggs exceeds the upper decision limit, then the 

population is expected to cause greater than 10% seed loss. When the number of eggs falls below the 

lower decision limit, damage is expected to be minimal. Sample a maximum of 59 trees, and calculate 

mean egg densities to compare with the critical density (10% seed loss). Sampling time ranges from 3 to 

18 h.

Notes

The relationship between the mean and variance is similar in both natural stands and seed orchards. This 

sequential sampling plan is quite reliable for classifying B. colfaxiana egg density except when densities 

approach the critical level, which results in 10% seed loss.
210

Fig. 2. A two-stage sequential sampling plan for classifying the mean density of Douglas-fir cone moth 

eggs per conelet relative a critical level defined as 0.6 per conelet. Three conelets are sample per 

tree, the minimum number of trees to sample is 10, and the maximum is 59. The normal confidence 

level is 98% (but see Fig. 3).

Figure 2 reprinted with permission from the Canadian Entomologist, January 15, 2001.

Figure
211

Spruce Seed Moth 

Cydia strobilella (L.) 

Lepidoptera: Tortricidae

Sweeney, J. D.; Miller, G. E.; Ruth, D. S. 1990. Sampling seed and cone insects in spruce. 

In: West, R. J., editor. Proceedings-cone and seed pest workshop. 1989 October. 

St. John’s, Newfoundland. Inf. Rep. N-X-274. Canadian Forest Service; 63-75.

Objectives

To determine if the percentage of damaged seeds per cone was related to C. strobilella density per conelet; 

to determine what sample size was necessary to estimate egg density; and to determine what infestation 

level was required to justify the use of control measures. 

Abstract

The spruce seed moth, Cydia strobilella (L.), is an important pest of seed orchards in Canada. The primary 

hosts are Engelmann, Picea engelmannii L., and white spruce, P. glauca (Moench) Voss, spruce although 

other species can be attacked. A study was conducted in interior British Columbia to determine if seed 

damage to white and Englemann spruce was related positively to C. strobilella infestation level and density 

per conelet.

The percentage of seeds damaged per cone was directly related to both the percentage of cones infested 

and density of C. strobilella per cone. Control measures were warranted if C. strobilella egg densities 

exceeded 0.8 per conelet. The optimal number of conelets to sample per tree was two. The number of 

sample trees required to estimate egg density with 90% confidence and 10% error was 223 to 509. This 

sampling intensity was practical for detailed, scientific studies only.

Sampling Procedure

Select 223 to 509 trees systematically from the area of concern. At each tree, collect two conelets from 

the upper to mid-crown when conelets are about half pendant. Conelets can be bulked and stored at 

-10o

C until dissected. The number of days required to sample this many conelets ranges from 5 to 12.

Dissect conelets with a pair of fine forceps under a stereoscopic microscope at 10 power magnification. 

Starting from the base of each cone, work distally pulling each cone scale away from the conelet 

searching for the presence of C. strobilella eggs, larvae, and damage. Seeds damaged by C. strobilella are 

packed with frass and easily distinguished from those fed upon by other seed pests. 
212

Seed and Cone Insects

Fatzinger, C. W.; Muse, H. D.; Miller, T.; Bhattacharyya, H. T. 1988. Estimating 

cone and seed production and monitoring pest damage in southern pine seed 

orchards. Res. Pap. SE-271. Asheville, NC: U. S. Department of Agriculture, 

Forest Service, Southern Research Station; 30 p.

Objective

To estimate orchard yields of female strobili and seeds; to quantify pest damage; to determine time of 

year when losses occur; and to develop life tables for female strobili.

Abstract

Field sampling procedures and computer programs are described for monitoring seed production and 

pest damage in southern pine seed orchards. The system estimates total orchard yields of female strobili 

and seeds, quantifies pest damage, determines times of year when losses occur, and produces life tables 

for female strobili. Four types of samples that are generally conducted were reviewed. An example is 

included to illustrate the sampling procedures and the operations of user-friendly computer programs.

Sampling Procedure

The sampling procedure described is intended to generate data for entering into an accompanying 

computer program. Due to their detail and our inability to present the computer program and appendixes 

from the original publication, only a survey of the sampling procedure is presented here. Please see the 

original publication (Fatzinger and others 1984) if further details are required.

Four distinct types of samples are obtained:

Selecting sample trees within the orchard: Sample 42 trees per orchard, consisting of 14 sample clones 

with three ramets (primary sampling units) per clone. 

Sampling female strobili in different portions of the crowns of sample trees: Count all clusters of firstyear female strobili (secondary sampling unit) on each sample tree. Accuracy is very important and can 

be improved by dividing the crown of each sample tree by aspect. Timing is also important and should 

coincide with female flowers emerging through the bud scales. Select and tag a 10% sample of flower 

clusters randomly in the southeast quadrant for subsequent observations.

Making periodic observations: Periodic observations are taken three times a year (late winter, late spring, 

and fall) to estimate the number and conditions of female strobili. A more intensive subsample on one 

of the three sample ramets in each sample clone is made during periods when major losses are known to 

occur.

Collecting seed samples: Collect six healthy cones from the tagged clusters in the southeast-crown 

quadrant from each sample tree at harvest (total = 252 cones). Identify each cone by tree and cluster 

number and store until they open. Some cones may need to be kiln-dried to facilitate opening. Remove 

all seeds and place in a paper envelope to be radiographed at a later date. Data are collected on number 
213

of seeds per cone, viability, proportion damaged, and cause of damage.

Note

A decision support system is available for evaluating different pest management strategies in slash pine, 

Pinus elliottii Engelm., seed orchards. Please refer to Fatzinger and Dixon (1986) for this information.

References

Fatzinger, C. W. 1984. Monitoring pest-caused losses of cones and seed in southern pine seed orchards. In: Yates, 

H. O. III., editor. Proceedings, cone and seed insects working party conference, IUFRO 1983 Athens, GA. 

Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station; 43 p.

Fatzinger, C. W.; Dixon, W. N. 1986. User’s guide for seedcalc: a decision support system for integrated pest 

management in slash pine seed orchards. Gen. Tech. Rep. SE-095. Asheville, NC: U.S. Department of 

Agriculture, Forest Service, Southern Research Station; 63 p. 
214
WOOD- AND BARK-BORING INSECTS

215

Northeastern Sawyer

Monochamus notatus (Drury) 

Coleoptera: Cerambycidae

Safranyik, L.; Raske, A. G. 1970. Sequential sampling plan for larvae of Monochamus in 

lodgepole pine logs. Journal of Economic Entomology 63: 1903-1906. 

Objective

To develop a sequential sampling plan for classifying the severity of damage from M. notatus on scattered 

and decked lodgepole pine, Pinus contorta Douglas var. latifolia Engelmann, logs. 

Abstract

The northeastern sawyer, Monochamus notatus (Drury), breeds primarily in dead and dying eastern white 

pine, Pinus strobus L., balsam fir, Abies balsamea L., and red spruce, Picea rubens Sarg. Young larvae feed on 

the inner bark, cambium, and outer sapwood, while older larvae bore deep into the heartwood. Damage 

results in windthrow, and degradation of sawlogs and pulpwood stored improperly. 

A sequential sampling plan is presented for classifying the severity of damage by larvae of M. notatus to 

decked and scattered lodgepole pine logs. Infestation classes are based on the number of borer holes per 

929 cm2

 of bark surface and classified as light, medium, or heavy infestations. Sampling is confined to the 

infested outer portion of the decks and to the 10 o’clock and 2 o’clock positions of individual logs (12 

o’clock being the top portion directly visible to the observer). 

Sampling Procedure

Before the sampling plan is used, it should be determined whether the whole deck or just the top, outer, 

or exposed logs are infested. There is a tendency for decks with small diameter logs to be partially 

infested, while decks with large logs or logs stacked loosely to be infested throughout. Only Monochamus

larval entrance holes are counted in the sample. Sampling is conducted in September when oviposition is 

completed and additional fresh attacks are unlikely.

Remove one sample of bark, 15.2 by 61 cm, with the long axis parallel to the grain from each log in the 

deck. The sample should be selected from the 10 o’clock or 2 o’clock position (i.e., 12 o’clock position 

being the top of the log directly visible to the observer). Count and record the number of entrance 

holes, reference the sequential sampling plan (Table 1), and continue sampling until a decision is met. 

Infestation severity will be classified into one of three categories: light (< 0.5/929 cm2

), medium (1.0/929 

cm2 -1.5/929 cm2), and heavy (> 3.0 entrance holes/929 cm2) (Table 1). 

Note

The original paper contains information regarding economic losses that is no longer applicable in today’s 

markets. 
216

Table

Table 1. Sequential sampling table for classifying the severity of Monochamus damage to lodgepole pine logs.

No. of 

sample units

Cumulative no. woodborer holes

Light 

≤

Medium 

≥

Medium 

≥

Heavy 

≥

1 ---

Continue sampling

--- ---

Continue sampling

12

2 --- --- --- 14

3 --- --- --- 16

4 --- --- --- 18

5 --- --- --- 20

6 --- --- --- 23

7 --- --- --- 25

8 0 --- --- 27

9 1 --- --- 29

10 1 --- --- 31

11 2 13 13 33

12 3 14 15 35

13 3 15 17 37

14 4 15 19 40

15 5 16 22 42

16 5 17 24 44

17 6 18 26 46

18 7 18 28 48

19 8 19 30 50

20 8 20 31 52

21 9 20 34 54

22 10 21 36 56

23 10 22 38 59

24 11 23 41 61

25 12 23 43 63

26 13 24 45 65

27 13 25 47 67

28 14 25 49 69

29 14 26 51 71

30 15 27 53 73

31 16 27 55 75

32 17 28 57 77

33 17 29 59 80

34 18 30 62 82

35 19 30 64 84

36 19 31 66 86

37 20 32 68 88

38 21 33 70 90

39 22 33 72 92

40 22 34 74 94

Table reprinted with permission from the Journal of Economic Entomology, January 15, 2001.
217

Oregon Fir Sawyer

Monochamus scutellatus oregonensis (LeConte) 

Coleoptera: Cerambycidae

Safranyik, L.; Raske, A. G. 1970. Sequential sampling plan for larvae of Monochamus in 

lodgepole pine logs. Journal of Economic Entomology 63: 1903-1906. 

Objective

To develop a sequential sampling plan for classifying the severity of damage from M. scutellatus oregonensis

on scattered and decked lodgepole pine, Pinus contorta Douglas var. latifolia Engelmann, logs. 

Abstract

The Oregon fir sawyer, Monochamus scutellatus oregonensis (LeConte), breeds primarily in dead and dying fir, 

Abies spp., trees. Young larvae feed on the inner bark, cambium, and outer sapwood, while older larvae 

bore deep into the heartwood. Damage results in windthrow, and degradation of sawlogs and pulpwood 

stored improperly. 

A sequential sampling plan is presented for classifying the severity of damage by larvae of M. scutellatus 

oregonensis to decked and scattered lodgepole pine logs. Infestation classes are based on the number of 

borer holes per 929 cm2

 of bark surface and classified as light, medium, or heavy infestations. Sampling 

is confined to the infested outer portion of the decks and to the 10 o’clock and 2 o’clock positions of 

individual logs (12 o’clock being the top portion directly visible to the observer). 

Sampling Procedure

Before the sampling plan is used, it should be determined whether the whole deck is infested or just 

the top, outer, or exposed logs. There is a tendency for decks with small diameter logs to be partially 

infested, while decks with large logs or logs stacked loosely being infested throughout. Only Monochamus

larval entrance holes are counted in the sample. Sampling is conducted in September when oviposition is 

completed and additional fresh attacks are unlikely. 

Remove one sample of bark, 15.2 by 61 cm, with the long axis parallel to the grain from each log in the 

deck. The sample should be selected from the 10 o’clock or 2 o’clock position (i.e., 12 o’clock position 

being the top of the log directly visible to the observer). Count and record the number of entrance 

holes, reference the sequential sampling plan (Table 1), and continue sampling until a decision is met. 

Infestation severity will be classified into one of three categories: light (< 0.5/929 cm2

), medium (1.0/929 

cm2 -1.5/929 cm2), and heavy (> 3.0 entrance holes/929 cm2) (Table 1; refer to p. 216). 

Note

The original paper also contains information regarding economic losses that are no longer applicable in 

today’s markets.
218

Western Pine Beetle

Dendroctonus brevicomis LeConte 

Coleoptera: Scolytidae

Dudley, C.O. 1971. A sampling design for the egg and first instar larval populations 

of the western pine beetle, Dendroctonus brevicomis (Coleoptera: Scolytidae). 

Canadian Entomologist 103: 1291-1313.

Objective

To develop a sampling method for the egg and larval stages of D. brevicomis.

Abstract

The western pine beetle, Dendroctonus brevicomis LeConte, is primarily a pest of ponderosa pine, Pinus 

ponderosa Dougl. ex Laws., in the western USA. Outbreaks are associated with factors that contribute to a 

lack of tree vigor such as crowding, mechanical damage, pathogens, or drought. The insect is capable of 

killing sections, strips, or patches of the cambium without causing tree death. Severe infestations cause 

growth loss and extensive tree mortality. 

This study was conducted in the central Sierra foothills of California in a mixed conifer cover type with a 

predominance of ponderosa pine. The distributions of attack, gallery lengths, eggs, and first instar larvae 

of an endemic population of D. brevicomis were described. Mean gallery length (GL) and mean larval 

densities (L) of mature populations are correlated significantly with mean attack density (A), and can be 

described by the simple linear regressions GL = 20.76 + 24.50A and L = 20.52 + 33.34A, respectively. 

The ratios of E/GL and L/GL are stable over a wide range of gallery length densities, and consequently 

egg-gallery length (E = - 2.63 + 1.64 GL) and larval-gallery length (L = - 5.59 + 1.32 GL ) correlations 

are highly significant.

An 88-cm2

 sampling unit was satisfactory for estimating egg or first instar populations. Taking four paired 

samples, evenly spaced along the infested bole of each of four trees per D. brevicomis generation, provided 

a sampling precision of 85%. Increasing the number of paired samples to 10 and the number of trees 

sampled per generation to 9 improved the precision to 90%. If trees are sampled before oviposition is 

complete, then the number of trees sampled per generation should be increased by one for each level of 

precision (i.e., 1%).

Sampling Procedure

Cut two circular 88-cm2

 sample bark cores at 1.5 m intervals along the infested bole. Extract samples by 

cutting through the bark with a portable circular saw. Carefully remove the cores, label and place them in 

a refrigerator prior to examination. 

To sample eggs, remove cores with a 2 cm thick sapwood backing attached to prevent desiccation. For 

most trees, collect 2 sets of egg samples 1 week apart. To sample larval gallery mines, cores are taken 6-8 

weeks after the initial attack when all viable eggs have hatched. These samples are taken adjacent to the 

earlier egg samples. Remove the sapwood backing and frass, and record the number of attacks, parent 
219

gallery length, and eggs and larval mines. Dissect all samples under 10 power magnification. 

Egg sampling requires more effort than larval sampling to estimate density with similar precision (Table 

VIII). A practical sampling method should provide population estimates with the highest precision 

pertinent to the objective of the investigation. For example, at least 10 samples (20 cores) would 

be required to estimate egg populations at a precision level of 90% (Table VIII). If time and cost 

considerations are important, and a lower precision is acceptable (i.e., 85%), a minimum of 4-5 samples 

(8-10 cores) can be taken from just the lower half of the bole. Only nine samples (18 cores) would be 

required to estimate larval densities with a precision level of 90% (Table VIII).

Table

Table VIII. The number of samples trees needed to estimate mean density of D. brevicomis eggs, larvae, 

and gallery length per dm2

 at selected sampling intensities (Ns) and precision levels. Blodgett Research 

Forest, Georgetwon, California 1967 (Modified from Dudley, 1971).

Ns

Variable Precision (%) 4 8 20

Eggs

95 52 44 40

90 13 11 10

85 6 5 4-5

Larvae

95 42 38 36

90 10-11 9-10 9

85 5 4 4

Gallery length

95 41 37 35

90 10 9 9

85 4-5 4 4

Table VIII reprinted with permission from the Canadian Entomologist, January 15, 2001.
220

Southern Pine Beetle

Dendroctonus frontalis Zimmermann 

Coleoptera: Scolytidae

Billings, R. F. 1988. Forecasting southern pine beetle infestation trends with pheromone 

traps. In Payne, T. L.; Saarenmaa, H., editors. Integrated control of Scolytid bark 

beetles: proceedings of IUFRO Working Party and International Congress of 

Entomology Symposium; 1988 July 4; Vancouver, BC, Canada; 295-306.

Objective

To develop an operational monitoring system for predicting the severity of regionally based D. frontalis

infestation trends. 

Abstract

The southern pine beetle, Dendroctonus frontalis Zimmermann, is the most damaging bark beetle in the 

southeastern USA. All species of indigenous pines are susceptible to attack except longleaf pine, Pinus 

palustris Mill., presumably due to its high resin flow. Mature, over-stocked stands of loblolly, P. taeda L., 

and shortleaf, P. echinata Mill, pines on poorly drained sites are most susceptible to infestation. During 

beetle epidemics, groups of host trees are typically killed, and termed “spots” to delineate from other 

infestations in close proximity.

A method of forecasting infestation trends of D. frontalis was developed in Texas and tested in 28 

locations throughout the southern USA. Multiple-funnel traps, baited with frontalin and turpentine, 

were deployed during the early spring to sample D. frontalis populations and its major clerid predator, 

Thanasimus dubius (F.). The proportions of D. frontalis to T. dubius, as well as mean numbers of D. frontalis

trapped per day, were correlated with county- and state-wide infestation trends that occurred the same 

year. A risk rating system was developed by plotting the mean number of D. frontalis trapped per day 

against the mean percentage of D. frontalis for each location (Fig. 1). Four levels of infestation severity 

were suggested: low (<6.0 D. frontalis/trap/d); declining (trap catches averaged <40 D. frontalis regardless 

of the number per day); increasing or high (trap catches averaged >35 D. frontalis/trap/d with D. frontalis 

>40%); and moderate or static (6-35 D. frontalis/trap/day with D. frontalis >40%). 

Severe outbreaks should be expected when early season trap catches exceed 75 D. frontalis per trap per day 

and contain 75% D. frontalis. A simple key was provided to forecast infestation levels from trap catch data 

(Table 3).

Sampling Procedure

Sample flying D. frontalis and T. dubius with multiple–funnel traps and the aggregation pheromone 

frontalin (Phero Tech Inc., Delta, BC). Bait each trap with two Eppendorf capsules of frontalin and a 

rapid-release rate (about 3.6 g/trap/d) of steam distilled turpentine (W. M. Barr Co., Memphis, TN) from 

loblolly pine. Dispense the turpentine in 250 ml amber bottles with an 18 cm long cotton wick (Fisher 

Scientific Intl., Springfield, NJ). 

Place two multiple-funnel traps in each of 3 separate pine stands located greater than 3.2 km apart within 

the area of concern in March or April. Collect insects weekly for 4 weeks. Calculate the mean percent D. 
221

frontalis, (D. frontalis / (D. frontalis + T. dubius) x 100), and the number of D. frontalis/trap/day for all traps 

and sampling dates within each area of concern. Refer to Table 3 to forecast infestation trends based on 

this data.

Figure and Table

Fig. 1. Mean values of SPB/trap/day and percent SPB for 28 localities within 11 southern states 

derived form early season pheromone surveys in 1987. State averages are indicated by state 

symbols without numbers, while specific localities are according to codes given in table 2. Data points 

in form of (+) indicate localities in which SPB infestation levels declined in 1987; ( ) indicates those 

with increasing SPB levels while those marked with () remained static, compared to 1986.
222

Figure 1 and Table 3 reprinted with permission from Virginia Polytechnic Institute and State 

University, Blacksburg, January 15, 2001.

Table 3. Guide for forecasting southern pine beetle infestation trends based on early season pheromone 

trap data. 

Required information: (a) Average percent SPB from spring survey where

percent SPB= total number of SPB x 100

 total number of SPB + clerids

Optional information: Average number of SPB/trap/day in last year’s spring survey from same general 

locality.

Answer the following questions to determine SPB infestation trend for the current year. 

 1. Is SPB/trap/day less than 6? If yes, got to 7

If no, go to 2

 2. Is percent SPB less than 40? If yes, go to 8

If no, go to 3

 3. Is percent SPB greater than 75 and numbe of SPB/ trap/day greater than 75? If yes to both, go to 12

If no to either, got to 4

 4. Is number of SPB/trap/day greater than 35? If yes, got 10

If no, go to 5

 5. Is number of SPB/trap/day known for the previous year fom the locality If yes, go to 6

If no, go to 11

 6. Compute ratio: #SPB/trap/day for current year

 #SPB/trap/day for previous year

 Is the raio less than 0.75?

 Is the ratio between 0.75 and 1.25?

 Is the ratio greater than 1.25?

If yes, go to 8

If yes, go to 9

If yes, go to 10

 7. The SPB infestation level is predicted to be low.

 8. The SPB infestation level is predicted to decline from last year’s level.

 9. The SPB infestation level is predicted to remain similar to last year’s level.

10. The SPB infestation level is predicted to increase from last year’s level.

11. The SPB infestation level is subject to moderate change from last year’s level,

 but the trend (increasing or declining) is unpredictable.

12. The SPB infestation is expected to increase to severe outbreak level.
223

Southern Pine Beetle

Dendroctonus frontalis Zimmermann 

Coleoptera: Scolytidae

Lih, M. P.; Stephen, F. M. 1987. Arkansas SPBMODEL – a computer simulation model. 

Protection Report R8-PR 5. Atlanta: U. S. Department of Agriculture, Forest 

Service, Southern Research Station; 2 p.

Objective

To provide a model useful at predicting D. frontalis spot growth for either research or management 

purposes.

Abstract

The southern pine beetle, Dendroctonus frontalis Zimmermann, is the most damaging bark beetle in the 

southeastern USA. All species of indigenous pines are susceptible to attack except longleaf pine, Pinus 

palustris Mill., presumably due to its high resin flow. Mature, over-stocked stands of loblolly, P. taeda L., 

and shortleaf, P. echinata Mill, pines on poorly drained sites are most susceptible to infestation. During 

beetle epidemics, groups of host trees are typically killed, and termed “spots” to delineate from other 

infestations in close proximity.

The Arkansas SPBMODEL predicts D. frontalis spot growth in currently infested stands over a three 

month period. This model estimates the number of infested trees, the cumulative number of dead trees, 

and the associated timber volume and dollar losses, in loblolly and shortleaf pine stands. The model uses 

data collected from 70 infested stands in Arkansas, Georgia, Louisiana and Mississippi, and had a mean 

absolute error of 13.3% for predicting the number of dead trees over a 92-d period.

Sampling Procedure

Required inputs:

1. Spot identification (for user’s future reference)

2. State in which infestation is located 

3. Date ground checked 

4. Desired number of days of prediction 

5. Percentage of shortleaf and loblolly pines in stand 

6. Mean d.b.h. of stand 

7. Mean pine and hardwood basal areas (BA) 

8. Number of trees currently infested with SPB 

9. Number of trees previously infested with SPB 

10. Data measurement units (standard or metric)

Optional inputs:
224

1. General d.b.h. distribution of the stand 

2. Predominant SPB lifestages (attacking beetles, eggs, larvae, pupae, and brood adults) 

present in trees at breast height 

3. Mean age of all pines 

4. Average radial tree growth over the last 5 years 

5. Desired temperature modification (°F)

6. Local stumpage prices for salvaged pine sawtimber and pulpwood.

The program predicts daily and weekly spot growth for the requested period of time including 

confidence intervals on the number of currently infested trees and cumulative number of dead trees. 

Options also allow the user to estimate volume and economic losses for the period of simulation based 

on the diameter distribution of infested trees.

Notes

The model assumes that spots will continue to grow. A personal computer (PC) version runs on any 

IBM-compatible computer (>286 with math coprocessor) with a minimum of 256K of memory, 

and MS-DOS 2.0. The model may also be accessed through the USDA Forest Service Data General 

computing system. Diskettes for the PC version and copies of the User’s Guide are available from the 

USDA Forest Service, Forest Pest Management, 2500 Shreveport Hwy., Pineville, LA 71360. A newer 

version of this model is being developed. 
225

Southern Pine Beetle

Dendroctonus frontalis Zimmermann 

Coleoptera: Scolytidae

Linit, M. J.; Stephen, F. M. 1978. Comparisons of methods for estimation of attacking 

adult populations of Dendroctonus frontalis. Journal of Economic Entomology 71: 

732-735.

Objective

To determine the reliability of three methods used to estimate attacking densities of D. frontalis. 

Abstract

The southern pine beetle, Dendroctonus frontalis Zimmermann, is the most damaging bark beetle in the 

southeastern USA. All species of indigenous pines are susceptible to attack except longleaf pine, Pinus 

palustris Mill., presumably due to its high resin flow. Mature, over-stocked stands of loblolly, P. taeda L., 

and shortleaf, P. echinata Mill, pines on poorly drained sites are most susceptible to infestation. During 

beetle epidemics, groups of host trees are typically killed, and termed “spots” to delineate from other 

infestations in close proximity.

Attacking adult densities of D. frontalis in loblolly pine were estimated by three procedures: X-ray analysis 

(XRAY), bark dissection to locate attacking adults (ADULT-DISS), and bark dissection to locate the entry 

point of attacks (ATK-SITE). Estimates of mean attacking density via ADULT-DISS and ATK-SITE 

methods were in close agreement. Analysis of XRAY estimates by one worker resulted in consistently 

lower estimates than either the ATK-SITE or ADULT-DISS method. Analysis of XRAY estimates by the 

second worker was variable and could not be attributed to inexperience on the part of that analyst. 

Each of the three methods provided reliable estimates of attacking density. The authors suggested 

the XRAY method would not differ from other methods if the analysts were more experience and 

consistent in their observations. Both the ADULT-DISS and XRAY methods required precise timing 

in regard to the stage of adult colonization. Samples must be taken after attacks are complete and prior 

to reemergence. Since all three methods yielded reliable estimates, the authors suggested the choice of 

which method to use should depend on the level of personnel training and their objectives.

Sampling Procedure

To collect samples, fell each tree and remove a log from the central portion of the infested bole 

prior to adult emergence. Cut 36 100-cm2

 circular samples, and remove them from each log with the 

sapwood attached to prevent beetles from falling out of the galleries prior to analysis. Store samples in a 

refrigerator when not being processed. 

X-ray determination of attacking adults (XRAY): X-ray each bark sample immediately following removal 

using a Faxitron 43805® or similar X-ray system. Two workers are needed to examine the X-rays. Count 

and record the number of attacking adults on each sample separately.

Dissection for attacking adults (ADULT-DISS): Remove the sapwood from the bark sample with a chisel 

and count all D. frontalis adults. Remove frass and resin from egg galleries by using forceps and a stiff 
226

brush. Egg galleries are searched extensively for the presence of attacking adults as are all holes and 

crevices on the bark surface. Count the total number of adults and record their sex.

Attack site determination (ATK-SITE): Locate attack sites on each bark sample by using a binocular 

microscope and the criteria of Stephen and Taha (1976). If the criteria are met, then count the suspected 

attacked site and multiply by two to account for monogamous pairs. 

Notes

In this study, samples were collected during mid-July from three loblolly pines 37-yr-old and 35-cm d.b.h. 

The sex ratio of attacking adults did not differ significantly from unity, supporting the premise that one 

male and one female are associated with each attack site. This assumption is necessary for the validity of 

the ATK-SITE method.

Reference

*Stephen, F. M.; Taha, H. A. 1976. Optimization of sampling effort for with-in tree populations of southern pine 

beetle and its natural enemies. Environmental Entomology 5: 1001-1007.
227

Southern Pine Beetle

Dendroctonus frontalis Zimmermann 

Coleoptera: Scolytidae

Stephen, F. M.; Taha, H. A. 1976. Optimization of sampling effort for within-tree 

populations of southern pine beetle and its natural enemies. Environmental 

Entomology 5: 1001-1007.

Objective

To optimize sampling effort required for estimating within-tree populations of D. frontalis.

Abstract

The southern pine beetle, Dendroctonus frontalis Zimmermann, is the most damaging bark beetle in the 

southeastern USA. All species of indigenous pines are susceptible to attack except longleaf pine, Pinus 

palustris Mill., presumably due to its high resin flow. Mature, over-stocked stands of loblolly, P. taeda L., 

and shortleaf, P. echinata Mill, pines on poorly drained sites are most susceptible to infestation. During 

beetle epidemics, groups of host trees are typically killed, and termed “spots” to delineate from other 

infestations in close proximity.

Areas in southern Arkansas were surveyed to determine optimum sample sizes as a function of attack 

density, egg gallery length, and total brood size. A series of permanent 2,000-cm2

 X-ray maps were made. 

They depicted D. frontalis life stages occurring at varying heights in trees of different sizes. The map 

data were stored as addressable grid cell values in a computer, and programs were written for randomly 

selecting a series of defined experimental units. From these observations, the relationship of sample 

number to sample unit size was determined, and a procedure was outlined for estimating D. frontalis

density. 

Sampling Procedure

Select 10 trees in each spot in either the pupal or callow (teneral) adult stage. Cut three logs containing 

approximately 2,000 cm2

 of bark surface area from each tree (n=30), taking one log each from the upper, 

middle and lower bole. Count and record the number of pupae and callow adults.

Measure each log to determine bark area, and identify sections to be placed on X-ray film for radiography. 

Count the number of attacks and any brood (live larvae, pupae or callow adults). Mark their locations 

with colored china markers to produce a map. Identify D. frontalis galleries on the original bark samples, 

and by cross-comparison between the samples and map, draw the galleries on the map. Measure the 

gallery lengths. Following identification and marking, a transparent grid with 2.5 cm squares on each side 

is placed over the entire map and all variables and their locations are measured and recorded. 

The values for each variable are stored on a computer by coordinates of each unit grid square (6.25 cm2

) 

on the maps. The computer program represents the map as a cylinder and then generates the random 

samples to be taken.
228

Procedure:

1. Depending on the variables to be measured, use equations in Table 2 of the original 

 publication to calculate desired sample sizes. 

2. Survey the infested area where population measurements are desired in order to estimate 

 the number of infested spots. 

3. Depending on the number of samples determined from Step 1, allocate a proportionate 

number to each spot depending on the number of infested trees in suitable stages of 

 brood development.

4. Samples should be collected from a minimum of three heights, which are divided evenly 

 along the infested portion of the tree bole.

Example: The average number of samples required to estimate attacking density was 107 100-cm2

samples, 20 500-cm2 samples or 10 1,000-cm2 samples (Table 3). Assume that 100-cm2 samples are used, 

there are three infested spots, and that the number of trees suitable for sampling in each spot is 10, 20 

and 2. Therefore, the 107 samples would be divided according to the proportion 5:10:1 (i.e., 33, 67 and 7 

samples, respectively).

Table

Table reprinted with permission from Environmental Entomology, January 15, 2001.

Table 3. Calcualted number of samples needed to estimate density of the given variables within 10% of 

the mean for 100 cm2 , and 1,000 cm2

 sample unit areas. Number of samples needed at the tower and 

upper 90% confidence intervals are also given.

No. of Samples

Sample unit area Variable Lower Average Upper

100 cm2

Attacks 72 107 158

Gallery Length 11 17 25

Total Brood 49 95 178

Parasites 212 385 675

Predators 429 650 971

500 cm2

Attacks 13 20 32

Gallery Length 4 6 9

Total Brood 12 26 56

Parasites 55 112 220

Predators 104 170 276

1000 cm2

Attacks 6 10 16

Gallery Length 2 4 6

Total Brood 7 15 34

Parasites 31 66 136

Predators 56 96 160
229

Southern Pine Beetle

Dendroctonus frontalis Zimmermann 

Coleoptera: Scolytidae

Stephen, F. M.; Taha, H. A. 1979. Area-wide estimation of southern pine beetle 

populations. Environmental Entomology 8: 850-855.

Objective

To expand existing techniques (Stephen and Taha 1976) to permit estimation of the absolute density of 

D. frontalis within a defined forest stand.

Abstract

The southern pine beetle, Dendroctonus frontalis Zimmermann, is the most damaging bark beetle in the 

southeastern USA. All species of indigenous pines are susceptible to attack except longleaf pine, Pinus 

palustris Mill., presumably due to its high resin flow. Mature, over-stocked stands of loblolly, P. taeda L., 

and shortleaf, P. echinata Mill, pines on poorly drained sites are most susceptible to infestation. During 

beetle epidemics, groups of host trees are typically killed, and termed “spots” to delineate from other 

infestations in close proximity.

This study was conducted in 800 ha of over-mature pine-hardwood dominated by loblolly and shortleaf 

pines in Arkansas. Aerial and ground survey methods were combined with within-tree sampling 

procedures for the purpose of estimating absolute numbers of D. frontalis over a discrete forest stand 

containing a series of spots. Using this survey and sampling technique, a procedure for determining the 

total area of infested bark within the stand was developed. Trees were selected from actively infested 

spots and sampled intensively to obtain density estimates of D. frontalis stages per unit area of infested 

bark. On average, the following numbers of 100-cm2

 samples were sufficient to produce an estimate with 

90% precision for measuring the number of attacks (107) and mature brood (95), and determining gallery 

length (17). The mean and variance per 100 cm2

 were calculated from all bark disks. The product of these 

estimates multiplied by the number of infested trees within the stand provided an estimate of the total 

number of D. frontalis for the stand.

Sampling Procedure

Aerial survey: A helicopter is used to detect D. frontalis spots (Thatcher and others 1982) that were 

subsequently ground checked. 

Ground survey: Record host tree species, d.b.h., stage of beetle development at breast height, and crown 

color for each tree sampled in the spot. Also, determine the average height, average height of the infested 

portion of the bole, and pine and hardwood basal areas. If necessary, measure radial growth or tree age 

depending on your objectives. Flag each tree with a particular color specific to that survey trip.

The date on infested bole lengths are necessary for calculation of infested bark area. Selected trees must 

be climbed or felled in order to obtain this information accurately.

Population sampling: Trees are selected from actively infested spots and sampled intensively to obtain 

density estimates of D. frontalis stages per unit area of infested bark (Stephen and Taha 1976). On 
230

average, the following numbers of 100-cm2 samples were sufficient to produce an estimate within 

10% of the mean 90% of the time for measuring the number of attacks (107), mature brood (95), and 

determining gallery length (17). 

Climb trees to minimize disturbance to the surrounding stand. Remove samples (3 per infested bole 

length), and process in the laboratory. Record the data for each variable and express that value per unit of 

infested bark area. 

Mathematical model for area-wide estimation of D. frontalis populations: To estimate total D. frontalis

numbers, calculate the mean and variance per 100 cm2

 from all of the bark disks collected. In addition, 

calculate the mean and variance of the infested bark area for each infested sample tree. The product 

of the means of these two estimates times the total number of infested trees in the stand provides an 

estimate of the total number of D. frontalis in the stand.

Determination of infested bark area: Procedures for estimating the average infested phloem area (bark 

area) of a tree are provided by Coulson and others (1976) and Foltz and others (1976).

Notes

The techniques presented here violate some statistical assumptions necessary in obtaining a completely 

random sample. Sample trees must be large enough to climb and the authors’ selection of three sample 

heights per tree evenly spaced along the infested bole is not random. The techniques are standardized and 

reproducible, which suggests that any bias introduced into these estimates is relatively constant.

References

Coulson, R. N.; Pulley, P. E.; Foltz, J. L.; Martin, W. C. 1976. Procedural guide for quantitatively sampling within-tree 

populations of Dendroctonus frontalis. Miscellaneous Publication 1267. College Station: Texas Agricultural 

Experiment Station. 26 p.

Foltz, J. L.; Mayyasi, A. M.; Pulley, P. E.; Coulson, R. N.; Martin, W. C. 1976. Host tree geometric models for use in 

southern pine beetle population studies. Environmental Entomology 5: 714-719.

* Stephen, F. M.; Taha, H. A. 1976. Optimization of sampling effort for within-tree populations of southern pine 

beetle and its natural enemies. Environmental Entomology 5: 1001-1007.

* Thatcher, R. C.; Mason, G. N.; Hertel, G. D.; Searcy, J. L. 1982. Detecting and controlling the southern pine beetle. 

Southern Journal of Applied Forestry 6: 153-159.
231

Southern Pine Beetle

Dendroctonus frontalis Zimmermann 

Coleoptera: Scolytidae

Thatcher, R. C.; Mason, G. N.; Hertel, G. D.; Searcy, J. L. 1982. Detecting and 

controlling the southern pine beetle. Southern Journal of Applied Forestry 6: 153-

159.

Objective

To summarize new and improved techniques for locating, evaluating, and treating D. frontalis infestations.

Abstract

The southern pine beetle, Dendroctonus frontalis Zimmermann, is the most damaging bark beetle in the 

southeastern USA. All species of indigenous pines are susceptible to attack except longleaf pine, Pinus 

palustris Mill., presumably due to its high resin flow. Mature, over-stocked stands of loblolly, P. taeda L., 

and shortleaf, P. echinata Mill, pines on poorly drained sites are most susceptible to infestation. During 

beetle epidemics, groups of host trees are typically killed, and termed “spots” to delineate from other 

infestations in close proximity.

Four USDA handbooks dealing with detection, evaluation, suppression and prevention of D. frontalis

infestations are summarized here. Topics include aerial detection and evaluation of spots (Billings and 

Doggett 1980), ground checking (Billings and Pace 1979), and methods for assigning control priorities. 

Aerial surveys are used to locate infestations. Each infestation is assigned a low, medium or high priority 

based on the color of infested tree foliage, the number of infested trees, and the threat to surrounding 

forests (Table 1). At each spot, a ground crew checks to determine if D. frontalis is the mortality agent. 

Following correct diagnosis, trees are catalogued according to the stage of beetle attack (Table 2). This 

information is then used to assign control priorities based on stand hazard ratings (Table 3). 

Sampling Procedure

Locate D. frontalis infestations by conducting an aerial survey via small fixed-wing aircraft or helicopter. 

These areas will appear as pockets of dead or dying pines commonly referred to as spots. Spots expand 

in late spring and early summer as adult beetles emerge from brood trees and attack adjacent pines at the 

leading edge of the infestation. An expanding spot viewed from the air appears most often as a group of 

red- and yellow-crowned trees. Trees of different crown colors (from red to yellow) indicate the direction 

of spread. Most red–crowned trees no longer contain viable brood. Yellow-crowned trees have been 

attacked more recently and often contain brood. Freshly attacked trees at the leading edge of the spot 

appear green and healthy. Therefore, you cannot distinguish uninfested from fresh-attacked trees without 

conducting a ground check.

Spots with 10 or more red- and yellow-crowned trees are assigned ground check priorities. Assign each 

spot a low, medium or high priority based on the color of infested tree foliage, the number and volume 

of infested trees, and the threat to surrounding forests (Table 1). Provide ground crews with a map 

indicating spot locations, sizes, and priorities for ground checking.

At each spot, a ground check crew determines if D. frontalis is the mortality agent by removal of bark 
232

sections from yellow-crowned trees and examining for the presence of S-shaped galleries. External 

symptoms such as the presence of pitch tubes on the bole, and reddish boring dust at the base of tree 

are also useful indicators of D. frontalis infestations. Following correct diagnosis, trees are catalogued 

according to the stage of beetle attack (Table 2).

This information is used to assign control priorities based on the proportion of stage 1 and 2 trees, stand 

density (basal area) and average d.b.h. (inches) (Table 3). For example, stands with stage 1 and 2 trees, 

high stand densities and large average diameters are assigned highest priority for control.

References

Billings, R. F; Pase, H. A. III. 1979. A field guide for ground checking southern pine beetle spots. Agric. Handb. 

558. Washington, DC: U. S. Department of Agriculture, Forest Service; 19 p.

Billings, R. F.; Doggett, C. 1980. An aerial observer’s guide to recognizing and reporting southern pine beetle spots. 

Agric. Handb. 560. Washington, DC: U. S. Department of Agriculture, Forest Service; 12 p.

Table

Table 1. Example of a table for setting southern pine beetle ground check priorities from the air, May 

through October. Choose the spot classification which best describes the spot. (From Agric. Handb. 560).

Priority for ground check Spot classification

Priority 1 (high) More yellow- than red-crowned trees. In dense natural pine stand 

or in area with past history of SPB outbreaks. Easy acess or high 

salvageable volume. In plantation or other high value area. Threat to 

cross property lines and high value stands.

Priority 2 (breakout) Yellow-crowned trees in spot previously reported controlled or inactive. 

Priority 3 (medium) More red- than yellow-crowned trees. Poor access or moderate 

salvageable volume.

Priority 4 (low) Few yellow-crowned trees. Infested pines surrounded by hardwoods 

or open land. Difficult to locate on ground because of small size or 

inaccessibility. In unmerchantable timber or with low salvageable 

volume.

Tables reprinted with permission from the Southern Journal of Applied Forestry, Volume 6, pp. 

153-159, published by the Society of American Foresters, 5400 Grosvenor Lane, Bethesda, MD 

20814-2198. Not for further reproduction.
233

Table 2. Symptoms associated with southern pine beetle-attacked trees in various stages of deterioration. 

(From Agric. Handb. 575). 

Symptom

Stage 1 Stage 2 Stage 3

Trees with fresh SPB 

attacks

Trees with developing 

SPB broods Vacated trees

Foliage color Green Green yellow trees with 

larvae; fade to yellow 

before brood emerges

Red, needles falling

Pitch tubes Soft, white or light pink Hardened, white Hard, yellow, crumbles 

easily

Checkered beetles Red, white, and black 

adults crawling on bark

Pink or red larvae 1⁄2 in 

long in SPB galleries

Larvae and pupae are 

purple; occur in pockets 

in th outer bark

Bark Tight, hard to remove Loose, peels easily Very loose, easily removed

Color or wood surface White, except close to 

new adult galleries

Light brown with blue 

or black sections

Dark brown to black

Exit holes None Few, associated with attacking adult reemergenceNumerous

Ambrosia beetle dust None White, localized areas 

around base of trees

Abundant around base 

of trees

Table 3. Guide to southern pine beetle spot growth and control priorities (May through October). (From 

Agric. Handb. 558) 

Key to spot growth Your spot’s classification Risk-rating points

A. Stage 1 trees Absent 

Present

0 

30

B. Stage 1 and 2 trees 1-10 

11-20 

21-50 

More than 50

0 

10 

20 

40

C. Pine basal area (ft2

/ac) (or 

stand density) at active head or 

heads or spot

Less than 80 (low density) 

80-120 (medium density) 

More than 120 (high density)

0 

10 

20

D. Stand class by average d.b.h. 

(in inches)

Pulpwood (9 in or less) 

Sawtimber (more than 9 in)

0 

10 1

 If total is 70-100, control priority is high. If total is 40-60, control priority is medium. If total is 0-30, control priority 

is low.

Tables reprinted with permission from the Southern Journal of Applied Forestry, Volume 

6, pp.153-159, published by the Society of American Foresters, 5400 Grosvenor Lane, 

Bethesda, MD 20814-2198. Not for further reproduction.
234

Mountain Pine Beetle

Dendroctonus ponderosae Hopkins 

Coleoptera: Scolytidae

Carlson, R. W.; Cole, W. E. 1965. A technique for sampling populations of the mountain 

pine beetle. Res. Pap. INT-20. Ogden, UT: U. S. Department of Agriculture, 

Forest Service, Intermountain Forest and Range Experiment Station; 13 p.

Objective

To determine the most appropriate sample size and location for estimating D. ponderosae densities within a 

tree.

Abstract

The mountain pine beetle, Dendroctonus ponderosae Hopkins, is the most destructive western bark beetle 

species in the USA and Canada. Lodgepole pine, Pinus contorta Dougl. ex Loud, is the primary host, 

although ponderosa, Pinus ponderosa Dougl. ex Laws., sugar, P. lambertiana Dougl., and western white, Pinus 

monitcola Dougl. ex D. Don, pines are also attacked. During epidemics, tree mortality is often extensive.

This study was conducted to develop suitable sampling techniques for estimating densities of D. 

ponderosae in lodgepole pine in Utah and Wyoming. The experimental design tested for variation between 

sample sizes, locations on the tree, and trunk diameters. Six sample units were superimposed in a nested 

fashion at each point sample, and included: 92.9-cm2

, 232.3-cm2 and 464.5-cm2 rectangular; and 92.9-cm2

and 232.2-cm2

 circular. The 92.9-cm2 and 232.3-cm2 rectangular samples were recommended. 

Sampling Procedure

Remove one bark sample either 92.9 or 232.2 cm2

 from the north and south aspect of the bole of an 

infested lodgepole pine 30.5 cm above and 30.5 cm below breast height. If more precision is required, 

then you should collect similar samples at all four aspects. Measure the density of successful attacks, 

length of egg galleries, and density of larvae, pupae or callow adults. The number of samples (trees) 

needed for a 20% standard error of the mean (SEM) at the 2/3 probability level was computed for each 

sample size and variable at breast height (Table 8). If the 92.9-cm2 sample is used, the zone can be divided 

into 6 levels of 10 cm each; three above and three below breast-height producing 24 sample locations 

(units). If the 232.2-cm2

 sample is used, the zone can be divided into 4 levels of 15.2 cm each, producing 

16 sample locations (units).

Notes

Results contained in this paper may only be applicable to areas that are bioclimatically similar. This 

sampling plan was developed on trees greater than 15 cm in diameter.
235

Table

Table 8. The numbe of trees required to be sampled for a 20 percent SME at 2⁄3 probability level based 

upon summed north and south bottom samples (rectangular samples only). 

Sample size

Density Plot 1/10 sq. ft. 1⁄4 sq. ft. 1⁄2 sq. ft. Proportional

Attack density Teton 9.13 3.36 2.42 3.13

Wassatch 7.76 4.22 3.63 3.08

Gallery density Teton 6.40 5.71 5.56 4.67

Wassatch 2.46 2.63 2.20 2.12

Brood density Teton 8.19 9.93 8.16 7.56

Wassatch 54.06 66.94 67.84 55.36
236

Mountain Pine Beetle

Dendroctonus ponderosae Hopkins 

Coleoptera: Scolytidae

Knight, F. B. 1960. Sequential sampling of Black Hills beetle populations. Res. Note 

RM-48. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Forest and Range Experiment Station; 8 p.

Objective

To develop a sequential plan for predicting trends in D. ponderosae populations.

Abstract

The mountain pine beetle, Dendroctonus ponderosae Hopkins, is the most destructive western bark beetle 

species in the USA and Canada. Lodgepole pine, Pinus contorta Dougl. ex Loud, is the primary host, 

although ponderosa, Pinus ponderosa Dougl. ex Laws., sugar, P. lambertiana Dougl., and western white, Pinus 

monitcola Dougl. ex D. Don, pines are also attacked. During epidemics, tree mortality is often extensive.

A procedure for sampling D. ponderosae in ponderosa pine was developed to predict infestation trends 

using a fixed sample size. The method required counting the number of live beetles in early July in 20 

15.4 by 15.4 cm bark samples removed from the tree 1.5-2.1 m above ground. One sample taken from 

the north and south aspect of 10 trees produced accurate estimates. The sequential sampling plan was 

referenced and sampling was continued until a decision was met. Infestations were classified as increasing, 

decreasing, or static. Accurate estimates are obtained with minimal effort using this procedure. However, 

in some infestations as many as 80 samples were required. If no decision was reached after 80 samples, 

infestations were classified as the greater of the two classes.

Sampling Procedure

Remove one 15.4 by 15.4 cm bark sample from the north and south aspect of the bole of an infested 

ponderosa pine 1.5 to 2.1 m above ground. After a minimum of 10 trees, reference the sequential 

sampling plan (Fig. 1), and continue sampling until a decision is met. If no decision is reached after 

80 samples, consider the population to be the higher of the two levels. Populations are classified as: 

increasing (emerging beetles will kill more trees than infested currently; >9 beetles/sample), static 

(emerging beetles will kill a similar number of trees as infested currently; 5-8 beetles/sample), or 

decreasing (emerging beetles will kill less trees than infested currently; <4 beetles/sample). 

Two sequential plans are available. One with a precision of 90% (Fig. 1) and the other with a lesser 

precision of 80% (Fig. 2). The sequential plan with lower precision may be useful in situations where a 

higher risk of error can be accepted.

Notes

Sampling must be done in early July before beetle flight commences. Trees are easy to find at that time 

because the foliage on all infested trees is discolored. Borderline cases should always be placed in the 

higher classifications when sampling is complete.
237

Figures
238

Figure 2. Sequential graph for sampling Black Hills beetle populations in 6” x 6” bark samples (80% 

confidence level). By the use of the same hypothetical situation as as in fig.1, the decision can be 

made after recording 36 sample counts.
239

Spruce Beetle

Dendroctonus rufipennis Kirby 

Coleoptera: Scolytidae

Knight, F. B. 1960. Sequential sampling of Engelmann spruce beetle infestations 

in standing trees. Res. Note RM-47. Fort Collins, CO: U.S. Department of 

Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment 

Station; 4 p.

Objective

To develop a sequential sampling procedure for estimating D. rufipennis populations and deciding if 

control is warranted.

Abstract

The spruce beetle, Dendroctonus rufipennis Kirby, is the most destructive pest of Engelmann, Picea engelmannii

Parry ex. Engelm, sitka, Picea sitchensis (Bong.) Carr., and white, Picea glauca (Moench) Voss, spruce in western 

North America. Typically, outbreaks have been associated with windthrow, or large accumulations of slash. 

Recently, a large-scale outbreak has resulted from the Routt Divide Blowdown in Colorado in 1997. Severe 

infestations cause growth loss and tree mortality. 

A sequential sampling plan was developed to estimate D. rufipennis populations and classify infestation levels. 

An early season procedure enables resource managers to make decisions about treatment in the current year. 

A late season procedure provides information for predicting infestation severity the following year. A 15.2 

by 15.2-cm bark sample was removed from the north and south aspect of each of 20 trees. The number of 

living D. rufipennis was counted and recorded, and the sequential sampling plan was referenced. Late season 

populations were predicted to increase, decrease, or remain static the following year.

Sampling Procedure

General procedures: Remove one 15.2 by 15.2-cm bark sample from the north and south aspect of each of 

20 trees. Bark samples are removed from the bole 1.2-2.1 m above ground. Count and record the number 

of living D. rufipennis.

Early sample: This sequential plan will help determine if immediate control is necessary. Sample at least 20 

trees, adding the number of beetles found in each sample. Reference the sequential sampling plan (Table 

1), and continue sampling until a decision is met. Infestations will be classified as requiring or not requiring 

control. If no decision is made after 80 samples, classify the infestation as requiring control. The limits for 

these classes are <4 beetles per sample for decreasing populations, and >5 beetles for static or increasing 

populations that warrant control operations.

Late sample: This sequential plan will help predict the severity of infestations the following year. Sample 

at least 20 trees, reference the sequential sampling plan (Table 2), and continue sampling until a decision is 

met. Populations are classified as: increasing (emerging beetles will kill more trees than infested currently; 

>4.5 beetles/sample), static (emerging beetles will kill a similar number of trees as infested currently; 2.5-3.5 

beetles/sample), or decreasing (emerging beetles will kill less trees than infested currently; <1.5 beetles/

sample). 
240

Notes

Data are collected from standing trees and do not consider the beetles that may occur in windthrown 

trees, which may be a significant portion of the population. The data follow a negative binomial 

distribution (for calculations, see Waters 1955). 

Reference

Waters, W. E. 1955. Sequential sampling in forest insect surveys. Forest Science 1: 68-79.

Tables

Table 1. Sequential sampling plan for Englemann spruce beetle infestations in standing trees 

for determining treatability (June counts). 

No. of samples 

examined

Cumulative n. of beetles No. of samples 

examined

Cumulative no. of beetles

Not treatable Treatable Not treatable Treatable

20 27 151 52 170 294

22 36 160 54 179 303

24 45 169 56 188 312

26 54 178 58 197 321

28 63 187 60 205 330

30 71 196 62 214 339

32 80 205 64 223 348

34 89 214 66 232 357

36 98 223 68 241 366

38 107 232 70 250 374

40 116 241 72 259 383

42 125 250 74 268 392

44 134 259 76 277 401

46 143 269 78 286 410

48 152 277 80 294 419

50 161 285
241

Table 2. Sequential sample plan for Englemann spruce beetle infestations in standing trees for predictin 

infestation trend (August-September counts). 

80Number of samples 

examined

Cumulative number of beetles

Decreasing Static Increasing

20 22 --- 137

22 26 --- 145

24 30 --- 153

26 34 --- 161

28 38 --- 169

30 42 --- 177

32 46 --- 185

34 50 --- 193

36 54 --- 201

38 58 89-91 209

40 61 93-99 217

42 65 97-107 225

44 69 101-115 233

46 73 105-123 241

48 77 109-131 249

50 80 112-139 256

52 84 116-147 264

54 88 120-155 272

56 92 124-163 280

58 96 128-171 288

30 99 132-179 296

62 103 136-187 304

64 107 140-195 312

66 111 144-203 320

68 115 148-211 328

70 119 151-219 335

72 123 155-227 343

74 127 159-235 351

76 131 163-243 359

78 135 167-251 367

80 138 170-258 375
242

Fir Engraver

Scolytus ventralis LeConte 

Coleoptera: Scolytidae

Berryman, A. A. 1968. Development of sampling techniques and life tables for the fir 

engraver, Scolytus ventralis (Coleoptera: Scolytidae). Canadian Entomologist 100: 

1138-1147.

Objective

To develop sampling methods for a detailed, scientific study of S. ventralis inhabiting individual trees; and 

to describe methods for detecting S. ventralis populations occupying large areas.

Abstract

The fir engraver, Scolytus ventralis LeConte, is an important pest of true firs, Abies spp., in western North 

America. Outbreaks are associated with stressed trees caused by drought, windthrow, or competition 

in combination with favorable weather conditions for insect development. Scolytus ventralis is capable of 

killing sections, strips or patches of cambium and phloem without causing tree death. Severe infestations 

cause growth loss and tree mortality. 

A method was presented for sampling and constructing life tables for S. ventralis inhabiting individual 

trees. A bark area of 464.5 cm2

 was determined to be an effective sample unit. The collection of two 

sample units from two vertical strata along the infested portion of the bole reduced within-tree variation. 

Trees were felled, and samples taken serially throughout the year. A sampling design was presented for 

increasing the precision of life tables, and for determining optimal sample sizes.

Sampling Procedure

The optimum sample unit was determined from the number of attacks on the tree bole according to 

Berryman (1968). Fell each sample tree. Cut a 30.5 cm long bolt from the infested portion of the tree 

at two strata. In the laboratory, remove a 464.5-cm2

 vertical strip of bark from the bolt, and count and 

record the number of attacks, total gallery length, eggs, larvae (by instar), pupae and adults. Larval instars 

are identified by their size, and their distance from the parent gallery (Ashraf 1968). 

Serial sampling involves the removal of a set of samples from a single tree at several points during 

development of S. ventralis. Sample once every three weeks during active periods (summer and spring) 

and once during the overwintering period. It is estimated that 10 sample sets would be required during 

the one year life cycle of S. ventralis. For each subsequent sample, cut 15 cm above or below the previous 

sample. Refer to Fig. 3 to determine the number of samples required to estimate S. ventralis density with 

known precision. 

Note

This paper includes detailed descriptions on estimating survivability of different life stages of S. ventralis.
243

Reference

Ashraf, M. 1968. Biological studies of Scolytus ventralis LeConte (Coleoptera: Scolytidae) with particular reference 

to the nematode parasite, Sulphuretylenchus elongatus (Massey). Pullman: Washington State University; Ph.D. 

dissertation. 81 p.

Berryman, A. A. 1968. Distributions of Scolytus ventralis attacks, emergence, and parasites in grand fir. Canadian 

Entomologist 100: 57-68.

Figure

Figure 3 reprinted with permission from the Canadian Entomologist, January 15, 2001.

Fig. 3. The number of trees (N) required to estimate the mean density of S. ventralis at two levels of 

precision (SE = 10% and 20% of mean) and at various mean densities per mean square foot.
244

Striped Ambrosia Beetle

Trypodendron lineatum (Olivier) 

Coleoptera: Scolytidae

Lindgren, B. S.; Borden, J. H. 1983. Survey and mass trapping of ambrosia beetles 

(Coleoptera: Scolytidae) in timber processing areas on Vancouver Island. 

Canadian Journal of Forest Research 13: 481-493.

Objectives

To develop a method of sampling and estimating overwintering populations of T. lineatum; to determine 

the spatial and temporal distribution of T. lineatum using pheromone-baited traps; and to determine the 

prevalence of T. lineatum that has been imported onto the site via infested host material.

Abstract

The striped ambrosia beetle, Trypodendron lineatum (Olivier), is a serious pest in timber yards of the 

Pacific Northwest. Losses result from degradation of lumber and plywood veneer, which are attacked on 

dryland log sorts. The distribution and population density of overwintering T. lineatum were determined 

by sampling beetles in the duff at four dryland log sorts in British Columbia, Canada. Significantly fewer 

beetles overwintered at the base of trees directly facing the sort than in any other quadrant (Fig. 1). 

Therefore, samples should be collected on the far side of the tree relative to the sort. 

The temporal and spatial distributions of flying T. lineatum were determined by catches in pheromonebaited traps. The heavy flight of T. lineatum in May and early June accounted for 79% of the total trap 

catch. At one dryland sort, the data from duff sampling and trapping were used to establish optimal trap 

placement for the subsequent year, and trapping effort was expanded into a mass trapping program. A 

reduction in damage as a result of removing T. lineatum was not evident. 

Sampling Procedure

Overwintering samples: Collect duff samples of 20 by 20 cm and 2-4 cm deep from at least 10 points 

15-20 m inside the forest margin. At each sample point, take a sample from the base of each designated 

tree in quadrant 3, which is always placed directly away from the sort (Fig. 1). Return samples to the lab 

and place them in 2 L milk cartons with an emergence jar attached. Collect and record the number of 

emergent beetles by sex for the first week, and every other day during the second week. 

Estimating overwintering populations of T. lineatum: Factors influencing the distribution of overwintering 

T. lineatum have been investigated thoroughly (Dyer and Kinghorn 1961). Based on data from that 

study, an equation for estimating the total population (N) of overwintering beetles was derived (see Fig. 

1). To estimate the total overwintering area (TOA), three beetle densities from transect samples (DT) 

are calculated by dividing the total number of beetles by sample area. DTs were then compared with 

the mean density of overwintering beetles from the permanent overwintering samples (DP) from that 

year, and the equation that yielded a value of DT closest to DP, but still smaller, was chosen. The 60 m 

distance used to calculate TOA was the maximum included in the total sample area for that equation. 
245

All duff samples are taken as close to trees as possible, and the mean number of beetles per square meter 

at each sort is considered the maximum density (X) at that sort. A high (Nh) and low (Nl) estimate is 

made by varying the area (A) within which the population density was X. For Nh, it is assumed that the 

density X extended to 0.9 m (i.e., where highest densities are encountered) from each tree within TOA

and for Nl to 0.45 m. To calculate the actual A, the mean basal area (BA) multiplied by the total number 

of stems (S) within TOA is deducted from the area of S circles with the radius 1 m + mean radius of the 

trees for Ah and 0.5 m + mean radius of trees for Al. The total number of beetles within A is then (Al x 

X) for Nl and (Ah x X) for Nh.

The density of T. lineatum in overwintering bark was about 60% of the density in the adjacent duff, and 

therefore density was Y = 0.6X. The mean area of 1 m of stem is calculated, and multiplied by S for 

the total stem area (SA) with beetle density Y. The total number of beetles overwintering in bark is then 

calculated as (SA x Y).

No direct information is available on the relative density of T. lineatum in duff greater than 0.9 m from 

trees. It is assumed that the density of overwintering beetles in the area Oal or Oah, outside the perimeter 

of Al and Ah, is (Z = 0.15 X). Oal and Oah are calculated by deducting the area of S circles with the 

radius 0.5 m + mean radius of trees and 1 m + mean radius of trees, respectively, from TOA. The total 

number of beetles within Oal is calculated as (Oal x Z) and within Oah as (Oah x Z). To obtain a low (Nl) 

and high estimate (Nh) of the population use the following equations:

Nl = (Al x X) + (SA x Y) + (OAl x Z) 

Nh = (Ah x X) + (SA x Y) + (OAh x Z)

Survey for temporal and spatial distribution: Setup 12 cylindrical sticky traps (Browne 1978) around 

the margin of dryland ports at each of four locations from early spring through fall. Bait one trap with 

lineatin, one with (±) – sulcatol, and one with s-(x)-sulcatol. The latter two are also baited with ethanol 

and a-pinene (Lindgren and others 1982). Placing traps near or inside stands of red alder, Alnus rubra

(Bong.), reduces the number destroyed by bears, Ursus americanus Pallas, but also decreases trap efficiency. 

Placement should depend on the level of bear interference. Check traps weekly and replace baits as 

needed. Count and record the number of T. lineatum. 

Log sampling: Sample incoming logs regularly as they are placed on log decks. Cut a 20 by 20 cm area 

of bark out with a chainsaw, remove with a chisel, and count and record the number of T. lineatum

attacks. Sampled logs should be marked and resampled at the sawmill. Managers can use this information 

to define problem areas in the forest or improperly handled logs in order to take appropriate control 

measures.

Note

Overwintering estimates were less reliable in areas with ill-defined forest margins. These methods have 

largely been replaced by the use of multiple-funnel traps which are much less labor intensive to use. 
246

References

Browne, L. E. 1978. A trapping system for the western pine beetle using attractive pheromones. Journal of 

Chemical Ecology 4: 261-275.

Dyer, E. D. A.; Kinghorn, J. M. 1961. Factors influencing the distribution of overwintering ambrosia beetles, 

Trypodendron lineatum (Oliv.). Canadian Entomologist 93: 746-759.

Lindgren, B. S.; Borden, J. H.; Gray, D. R.; Lee, P. C.; Palmer, D. A.; Chong, L. 1982. Evaluation of two trap log 

techniques for ambrosia beetles in timber processing areas. Journal of Economic Entomology 75: 577-586.

Figure

Fig. 1. Diagram illustrating areas used for estimating overwintering populations of T. lineatum at four 

dryland sorts on Vancouver Island. See text for explanation of symbols.

Figure 1 reprinted with permission from the Canadian Journal of Forest Research, January 15, 

2001. 
GLOSSARY

247

Glossary

basal area (BA) The cross-sectional area of a tree at 1.37 m above ground level; 

usually accumulated on a per acre basis for all trees and used as an 

indicator of stand density.

BAF Basal area factor; dependent on the sighting angle arbitrarily 

selected for a prism used in point sampling; e.g., in the eastern 

United States a sighting angle of 104.18 min or BAF 10 is 

commonly used. Any tree tallied within the plot represents 10 ft2

of basal area per acre.

codominant A tree with crown forming part of the general canopy level; 

receiving full sunlight from above and little from the sides.

d.b.h. Diameter at breast height; the diameter of a tree at 1.37 m above 

ground level on the uphill side.

dominant A tree with crown extending above the general canopy level formed 

by codominants; receiving full sunlight from above and partly from 

the sides.

frass Solid larval excrement exuded from the anus.

fundatrix (pl., fundatrices) In aphids, the wingless, parthenogenetic female that emerges in 

spring from overwintering eggs.

gallicola (pl., gallicolae) Gall-forming stages (insects).

instar The period or stage between molts during the larval stage; usually 

numbered to designate the various periods (typically five or six); 

e.g., the first instar occurs between egg hatch (eclosion) and the first 

molt.

integrated pest management 

(IPM) programs

The use of multiple techniques that are effective, economicallyviable and ecologically compatible for maintaining populations 

below a certain threshold.

intermediate A tree with crown extending into the general canopy level; receiving 

little direct sunlight from above and none from the side.

oviposition The act of laying eggs.

REFERENCES

249

References

Allen, D. C.; Abrahamson, L. P.; Eggen, D. A.; Lanier, G. N.; Swier, S. R.; Kelley, R. S.; Auger, M. 1986. Monitoring 

spruce budworm (Lepidoptera: Tortricidae) populations with pheromone-baited traps. Environmental 

Entomology 15: 152-165.

Allen, D. C.; Dorais, L.; Kettela, E. G. 1984. Survey and detection. In: Schmitt, D. M.; Grimble, D. G.; Searcy, J. L. 

editors. Spruce budworm handbook: managing the spruce budworm in eastern North America. Agric. 

Handb. 620. Washington, DC: U.S. Department of Agriculture, Forest Service; 21-36.

Amman, G. D. 1969. A method of sampling the balsam woolly aphid on Fraser fir in North Carolina. Canadian 

Entomologist 101: 883-889.

Ashraf, M. 1968. Biological studies of Scolytus ventralis LeConte (Coleoptera: Scolytidae) with particular reference 

to the nematode parasite, Sulphuretylenchus elongatus (Massey). Pullman: Washington State University; Ph.D. 

dissertation. 81 p.

Balch, R. E.; Underwood, G. R. 1950. The life history of Pineus pinifoliae (Fitch) (Homoptera: Adelgidae) and its 

effect on white pine. Canadian Entomologist 82: 117-123.

Barbosa, P.; Wagner, M. R. 1989. Introduction to forest and shade tree insects. San Diego: Academic Press, Inc.; 639 

p.

Batzer, H. O.; Jennings, D. T. 1980. Numerical analysis of a jack pine budworm outbreak in various densities of jack 

pine. Environmental Entomology 9: 514-524.

Batzer, H. O.; Martin, M. P.; Mattson, W. J.; Miller, W. E. 1995. The forest tent caterpillar in aspen stands: 

distribution and density estimation for four life stages in four vegetation strata. Forest Science 41: 99-121.

Beckwith, R. C.; Kemp, W. P. 1984. Shoot growth models for Douglas-fir and grand fir. Forest Science 30: 743-746.

Benjamin, D. M. 1955. The biology and ecology of the red-headed pine sawfly. Tech. Bull. 1118. Washington, DC: 

U.S Department of Agriculture, Forest Service; 57 p. Berryman, A. A. 1968. Development of sampling techniques and life tables for the fir engraver, Scolytus ventralis

(Coleoptera: Scolytidae). Canadian Entomologist 100: 1138-1147.

Berryman, A. A. 1968. Distributions of Scolytus ventralis attacks, emergence, and parasites in grand fir. Canadian 

Entomologist 100: 57-68.
250

Billings, R. F. 1988. Forecasting southern pine beetle infestation trends with pheromone traps. In Payne, T. L.; 

Saarenmaa, H., editors. Integrated control of Scolytid bark beetles: proceedings of IUFRO Working Party 

and International Congress of Entomology Symposium; 1988 July 4; Vancouver, BC, Canada; 295-306.

Billings, R. F.; Doggett, C. 1980. An aerial observer’s guide to recognizing and reporting southern pine beetle spots. 

Agric. Handb. 560. Washington, DC: U. S. Department of Agriculture, Forest Service; 12 p.

Billings, R. F; Pase, H. A. III. 1979. A field guide for ground checking southern pine beetle spots. Agric. Handb. 

558. Washington, DC: U. S. Department of Agriculture, Forest Service; 19 p.

Binns, M. R.; Bostanian, N. J. 1990. Robustness in empirically based binomial decision rules for integrated pest 

management. Journal of Economic Entomology 83: 420-427.

Browne, L. E. 1978. A trapping system for the western pine beetle using attractive pheromones. Journal of 

Chemical Ecology 4: 261-275.

Buss, L. J.; McCullough, D. G.; Ramm, C. W. 1999. Comparison of three egg mass survey methods in relation to 

gypsy moth (Lepidoptera: Lymantriidae) defoliation in Michigan. Environmental Entomology 28: 485-495.

Carlson, R. W.; Cole, W. E. 1965. A technique for sampling populations of the mountain pine beetle. Res. Pap. 

INT-20. Ogden, UT: U. S. Department of Agriculture, Forest Service, Intermountain Forest and Range 

Experiment Station; 13 p.

Carolin, V. M.; Coulter, W. K. 1972. Sampling populations of western spruce budworm and predicting defoliation 

on Douglas-fir in eastern Oregon. Res. Pap. PNW-149. Portland: U.S. Department of Agriculture, Forest 

Service, Pacific Northwest Forest and Range Experiment Station; 38 p.

Carolin, V. M. Jr.; Honing, F. W. 1972. Western spruce budworm. Pest Leafl. 53. Washington, DC: U.S. Department 

of Agriculture, Forest Service; 8 p.

Carolin, V. M.; Johnson, N. E.; Buffam, P. E.; McComb, D. 1964. Sampling egg populations of western hemlock 

looper in coastal forests. Res. Pap. PNW-14. Portland, OR: U.S. Department of Agriculture, Forest Service, 

Pacific Northwest Research Station; 15 p.

Carter, J. L.; Ravlin, F. W. 1995. Evaluation of binomial egg mass sampling plans for low density gypsy moth 

populations in continuously forested habitats. Journal of Economic Entomology 88: 890-896.

Carter, J. L.; Ravlin, F. W.; Gray, D. R.; Carter, M. R.; Coakley, C. W. 1994. Foliage presence and absence effect on 

gypsy moth (Lepidoptera: Lymantriidae) egg mass sample counts and the probability of exceeding action 

thresholds with foliage present. Journal of Economic Entomology 87: 1004-1007.

Cielsa, W. M.; Bousfield, W. E. 1974. Forecasting potential defoliation by larch casebearer in the northern Rocky 

Mountains. Journal of Economic Entomology 67: 47-51.

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.

Coffelt, M. A.; Schultz, P. B. 1994. Within-tree distribution and a fixed-precision-level sampling plan for the 

orangestriped oakworm (Lepidoptera: Saturniidae). Journal of Economic Entomology 87: 382-388.

Cole, W. E. 1960. Sequential sampling in spruce budworm control projects. Forest Science 6: 51-59.

Connola, D. P.; Waters, W. E.; Nason, E. R. 1959. A sequential sampling plan for red-pine sawfly Neodiprion nanulus

Schedl. Journal of Economic Entomology 52: 600-602.

Connola, D. P.; Waters, W. E.; Smith, W. E. 1959. The development and application of a sequential sampling plan 

for forest tent caterpillar in New York. Bull. No. 366, Albany: New York State Museum; 22 p.
251

Coulson, R. N.; Pulley, P. E.; Foltz, J. L.; Martin, W. C. 1976. Procedural guide for quantitatively sampling within-tree 

populations of Dendroctonus frontalis. Miscellaneous Publication 1267. College Station: Texas Agricultural 

Experiment Station. 26 p.

Coulson, R. N.; Witter, J. A. 1984. Forest entomology—ecology and management. New York: John Wiley and Sons 

Inc.; 669 p.

Dahlsten, D. L.; Rowney, D. L.; Lawson, A. B. 1998. IPM helps control elm leaf beetle. California Agriculture 52: 

17-23.

Daterman, G. E. 1974. Synthetic sex pheromone for detection survey of European pine shoot moth. Res. Pap. 

PNW-180. Corvallis, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research 

Station; 12 p. 

Daterman, G. E. 1978. Monitoring and detection. In: Brooks, M. H.; Stark, R. W.; Campbell, R. W., editors. The 

Douglas-fir tussock moth: a synthesis. Tech. Bull. 1585. Washington, DC: U.S. Department of Agriculture, 

Forest Service; 99-102.

Dimond, J. B. 1974. Sequential surveys for the pine leaf chermid, Pineus pinifoliae. Tech. Bull. 68. Orono, ME: 

University of Maine, Agricultural Experiment Station; 15 p.

Dobesberger, E. J. 1989. A sequential decision plan for the management of eastern hemlock looper, Lambdina 

fiscellaria fiscellaria (Lepidoptera: Geometridae), in Newfoundland. Canadian Journal of Forest Research 19: 

911-916.

Dobesberger, E. J.; Lim, K. P. 1983. Required sample size for early instar spruce budworm, Choristoneura fumiferana

(Lepidoptera: Tortricidae), in Newfoundland. Canadian Entomologist 115: 1523-1527.

Dreistadt, S.H. 1987. Monitoring honeydew excretion in the field as a method of sampling Illinoia liriodendri

(Homoptera: Aphididae) infesting Liriodendron tulipiferia. Journal of Economic Entomology 80: 380-383.

Dreistadt, S. H.; Flint, M. L. 1995. Landscape pest monitoring methods and training managers to use them. Journal 

of Arboriculture 21: 1-6.

Dudley, C.O. 1971. A sampling design for the egg and first instar larval populations of the western pine beetle, 

Dendroctonus brevicomis (Coleoptera: Scolytidae). Canadian Entomologist 103: 1291-1313.

Dyer, E. D. A.; Kinghorn, J. M. 1961. Factors influencing the distribution of overwintering ambrosia beetles, 

Trypodendron lineatum (Oliv.). Canadian Entomologist 93: 746-759.

Fatzinger, C. W. 1984. Monitoring pest-caused losses of cones and seed in southern pine seed orchards. In: Yates, 

H. O. III., editor. Proceedings, cone and seed insects working party conference, IUFRO 1983 Athens, GA. 

Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station; 43 p.

Fatzinger, C. W.; Dixon, W. N. 1986. User’s guide for seedcalc: a decision support system for integrated pest 

management in slash pine seed orchards. Gen. Tech. Rep. SE-095. Asheville, NC: U.S. Department of 

Agriculture, Forest Service, Southern Research Station; 63 p. 

Fatzinger, C. W.; Muse, H. D.; Miller, T.; Bhattacharyya, H. T. 1988. Estimating cone and seed production 

and monitoring pest damage in southern pine seed orchards. Res. Pap. SE-271. Asheville, NC: U. S. 

Department of Agriculture, Forest Service, Southern Research Station; 30 p.

Fettig, C. J.; Berisford, C. W. 1999. A comparison of three common indices for estimating Nantucket pine tip moth 

damage in the field. Journal of Entomological Science 34: 203-209.

Fettig, C. J.; Salom, S. M. 1998. Comparisons of two trapping methods for Hylobius pales (Coleoptera: Curculionidae) 

in Virginia. Environmental Entomology 27: 572-577.
252

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.

Fidgen, J. G.; Teerling, C. R.; McKinnon, M. L. 1994. Intra- and inter-crown distribution of the eastern spruce gall 

adelgid, Adelges abietis (L.), on young white spruce. Canadian Entomologist 126: 1105-1110.

Fleischer, S.; 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.

Fleischer, S. J.; Ravlin, F. W.; Reardon, R. C. 1991. Implementation of sequential sampling plans for gypsy moth 

(Lepidoptera: Lymantriidae) egg masses in eastern hardwood forests. Journal of Economic Entomology 

84: 1100-1107.

Foltz, J. L.; Knight, F. B.; Allen, D. C.; Mattson, W. J., Jr. 1968. A technique for sampling populations of the jackpine budworm. Forest Science 14: 277-281.

Foltz, J. L.; Mayyasi, A. M.; Pulley, P. E.; Coulson, R. N.; Martin, W. C. 1976. Host tree geometric models for use in 

southern pine beetle population studies. Environmental Entomology 5: 714-719.

Fowler, G. W.; Simmons, G. A. 1982. Spruce budworm egg mass density on balsam fir: low to extreme population 

levels (Lepidoptera: Tortricidae). Great Lakes Entomology 15: 277-286.

Gansner, D. A.; Herrick, O. W.; Ticehurst, M. 1985. A method for predicting gypsy moth defoliation from egg mass 

counts. Northern Journal of Applied Forestry 2: 78-79.

Gargiullo, P. M.; Berisford, C. W. 1981. Sampling for pine tip moths—a procedural guide. Res. Bull. 272. Athens: 

The University of Georgia; 25 p.

Gargiullo, P. M.; Berisford, C. W.; Pienaar, L. V. 1983. Two-stage cluster sampling for pine tip moths. Environmental 

Entomology 112: 81-90.

Grimble, D. G.; Kasile, J. D. 1974. A sequential sampling plan for saddled prominent eggs. Applied Forestry 

Research Inst. Rep. 15. Syracuse, NY: State University of New York, College of Environmental Science 

and Forestry; 15 p.

Hard, J. S. 1971. Sequential sampling of hemlock sawfly eggs in southeast Alaska. Res. Note PNW-142. Portland, 

OR: U.S. Department of Agriculture, Forest Service; 9 p.

Harris, J. W. E. 1963. Sampling the egg stages of the two-year-cycle spruce budworm near Babine Lake, British 

Columbia. Forestry Chronicle 39: 199-204.

Harris, J. W. E. 1977. Egg-sampling for western spruce budworm on Douglas-fir. Res. Notes 33. Canadian Forest 

Service; 26-27.

Herbert, 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. 

Hunt, D. W. A.; Raffa, K. F.. 1989. Attraction of Hylobius radicis and Pachylobius picivorus (Coleoptera: Curculionidae) 

to ethanol and turpentine in pitfall traps. Environmental Entomology 18: 351-355.

Ives, W. G. H.; Warren, G. L. 1965. Sequential sampling for white grubs. Canadian Entomologist 97: 596-604.

Jobin, L. J.; Coulombe, C. 1988. The Multi-Pher® insect trap. Inf. Leafl. LFC-24E. Saint-Foy, PQ: Canadian Forest 

Service, Quebec Region; 8 p.

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 
253

Economic Entomology 92: 638-643.

Knight, F. B. 1960. Sequential sampling of Black Hills beetle populations. Res. Note RM-48. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station; 8 p.

Knight, F. B. 1960. Sequential sampling of Engelmann spruce beetle infestations in standing trees. Res. Note RM47. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest and Range 

Experiment Station; 4 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.

Kulman, H. M.; Hodson, A. C. 1962. A sampling unit for the jack-pine budworm, Choristoneura pinus. Journal of 

Economic Entomology 55: 801-802.

Kuno, E. 1991. Sampling and analysis of insect populations. Annual Review of Entomology 36: 285-304.

Lance, D. R.; Elkinton, J. S.; Schwalbe, C. P. 1986. Components of density-related stress as potential determinants 

of population quality in the gypsy moth (Lepidoptera: Lymantriidae). Environmental Entomology 15: 914-

918.

Lasota, J. A.; Shetlar, D. J. 1986. Assessing seasonal and spatial abundance of Adelges cooleyi (Gillette) (Homoptera: 

Adelgidae) by various sampling techniques. Environmental Entomology 15: 254-257.

Liang, Q.; Otvos, I. S.; Bradfield, G. E. 1996. Distribution pattern and sampling of eggs of the western hemlock 

looper (Lepidoptera: Geometridae) in mature western hemlock stands. Journal of Economic Entomology 

89: 1531-1536.

Liebhold, A. M.; Elkinton, J. S. 1988. Techniques for estimating the density of late-instar gypsy moth, Lymantria 

dispar (Lepidoptera: Lymantriidae), populations using frass drop and frass production measurements. 

Environmental Entomology 17: 381-384.

Liebhold, A. M.; Elkinton, J. S.; Zhou, G.; Hohn, M. E.; Rossi, R. E.; Boettner, G. H.; Boettner, C. W.; Burnham C.; 

McManus, M. L. 1995. Regional correlation of gypsy moth (Lepidoptera: Lymantriidae) defoliation with 

counts of egg masses, pupae, and male moths. Environmental Entomology 24: 193-203.

Liebhold, A.; Luzader, E.; Reardon, R.; Roberts, A.; Ravlin, F. W.; Sharov, A.; Zhou, G. 1998. Forecasting gypsy 

moth (Lepidoptera: Lymantriidae) defoliation with a geographical information system. Journal of 

Economic Entomology 91: 464-472.

Liebhold, A.; Thorpe, K.; Ghent, J.; Lyons, D. B. 1994. Gypsy moth egg mass sampling for decision-making: a user’s 

guide. NA-TP-04-94. Morgantown, WV: U.S. Department of Agriculture, Forest Service, Northeastern 

Area; 12 p.

Lih, M. P.; Stephen, F. M. 1987. Arkansas SPBMODEL – a computer simulation model. Protection Report R8-PR 

5. Atlanta: U. S. Department of Agriculture, Forest Service, Southern Research Station; 2 p.

Lindgren, B. S.; Borden, J. H. 1983. Survey and mass trapping of ambrosia beetles (Coleoptera: Scolytidae) in timber 

processing areas on Vancouver Island. Canadian Journal of Forest Research 13: 481-493.

Lindgren, B. S.; Borden, J. H.; Gray, D. R.; Lee, P. C.; Palmer, D. A.; Chong, L. 1982. Evaluation of two trap log 

techniques for ambrosia beetles in timber processing areas. Journal of Economic Entomology 75: 577-586.

Linit, M. J.; Stephen, F. M. 1978. Comparisons of methods for estimation of attacking adult populations of 

Dendroctonus frontalis. Journal of Economic Entomology 71: 732-735.

Lynch A. M.; Fowler, G.W.; Simmons, G. A. 1990. Sequential sampling plans for spruce budworm (Lepidoptera: 

Tortricidae) egg mass density using Monte Carlo simulation. Journal of Economic Entomology 83: 1479-1484.
254

Lyons, L. A. 1964. The spatial distribution of two pine sawflies and methods of sampling for the study of 

population dynamics. Canadian Entomologist 96: 1373-1407.

Mangini, A.; Carlton, C.; Perry, R. W.; Hanula, J. L. 1994. Seed, cone, regeneration, and defoliating insects in forest 

ecosystem management. Gen. Tech. Rep. 50. U.S. Department of Agriculture, Forest Service; 154-161.

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.

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. 1979. How to sample larvae of the Douglas-fir tussock moth. Agric. Handb. 547. Washington, DC: 

U.S. Department of Agriculture, Forest Service; 15 p.

Mason, R. R. 1987. Frequency sampling to predict densities in sparse populations of the Douglas-fir tussock moth. 

Forest Science 33: 145-156.

Mason, R. R.; Wickman, B. E; Paul, H. G. 1989. Sampling western spruce budworm by counting larvae on lower 

crown branches. Res. Note PNW-486. Portland, OR: U.S. Department of Agriculture, Forest Service, 

Pacific Northwest Research Station; 8 p.

McGraw, J. R.; Hain, F. P. 1979. Spruce spider mite sampling system. Forest Research Notes. North Carolina 

opment; 30-31.

McKnight, M. E. 1969. Estimating defoliation of Douglas-fir and white fir by the western budworm. Res. Note 

RM-144. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest and 

Range Experiment Station; 2 p.

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.

Miller, C. A. 1958. The measurement of spruce budworm populations and mortality during the first and second 

larval instars. Canadian Journal of Zoology 36: 409-422.

Miller, C. A.; Greenbank, D. O.; Kettela, E. G. 1979. Possible use of canopy light traps in predicting spruce 

budworm egg-mass counts. Res. Notes 35. Ottawa: Canadian Forest Service; 29-30.

Miller, C. A.; Kettela, E. G. 1972. An additional note on sampling overwintering spruce budworm larvae. Inf. Rep. 

M-X-34. Fredericton, NB: Canadian Forest Service; 6 p.

Miller, C. A.; Kettela, E. G.; McDougall, G. A. 1971. A sampling technique for overwintering spruce budworm and 

its applicability to population surveys. Inf. Rep. M-X-25. Fredericton, NB: Canadian Forest Service; 4 p. 

Miller, C. A.; McDougall, G. A. 1968. A new sampling technique for spruce budworm larvae. Res. Note 24. Ottawa: 

Canadian Forest Service, Rural Development; 30-31.

Miller, G. E. 1986. 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.
255

Miller, G. E. 1986. Damage prediction for Contarinia oregonensis Foote (Diptera: Cecidomyiidae) in Douglas-fir seed 

orchards. Canadian Entomologist 118: 1297-1306.

Miller, W. E. 1960. The European pine shoot moth: relationship between proportion of trees infested and number 

of insects per tree. Journal of Forestry 58: 647-648.

Miller, W. E.; Wilson, L. F. 1964. Composition and diagnosis of pine tip moth infestations in the southeast. Journal 

of Economic Entomology 57: 722-726.

Nealis, V. G.; Lysyk, T. J. 1988. Sampling overwintering jack pine budworm, Choristoneura pinus pinus Free. 

(Lepidoptera: Tortricidae), and two of its parasitoids (Hymenoptera). Canadian Entomologist 120: 1101-

1111.

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.

Pedigo, L. P.; Buntin, G. D. 1994. Handbook of sampling methods for arthropods in agriculture. Boca Raton, FL: 

CRC Press Co.; 714 p.

Pendrel, B. A. 1985. Population distribution of Jack pine budworm—1984 described through pheromone trapping. 

Tech. Note 133. Canadian Forest Service, Maritimes Forest Research Centre; 4 p.

Prebble, M. L. 1975. Aerial control of forest insects in Canada. Ottawa: Canadian Forest Service; 330 p.

Raffa, K. F.; Hunt, D. W. A. 1988. Use of baited pitfall traps for monitoring pales weevil, Hylobius pales (Coleoptera: 

Curculionidae). Great Lakes Entomology 21: 123-125.

Ramaswamy, S. B.; Cardé, R. T. 1982. Nonsaturating traps and long-life attractant lures for monitoring spruce 

budworm males. Journal of Economic Entomology 75: 126-129.

Régnière, J. J.; Sanders, C. J. 1983. Optimal sample size for the estimation of spruce budworm (Lepidoptera: 

Tortricidae) populations on balsam fir and white spruce. Canadian Entomologist 115: 1621-1626.

Rieske, L. K; Raffa, K. F. 1993. Potential use of baited pitfall traps in monitoring pine root weevil, Hylobius pales, 

Pachylobius picivorus, and Hylobius radicis (Coleoptera: Curculionidae) populations and infestation levels. 

Journal of Economic Entomology 86: 475-485.

Safranyik, L.; Raske, A. G. 1970. Sequential sampling plan for larvae of Monochamus in lodgepole pine logs. Journal 

of Economic Entomology 63: 1903-1906. 

Sanders, C. J. 1980. A summary of current techniques used for sampling spruce budworm populations and 

estimating defoliation in eastern Canada. Rep. O-X-306. Canadian Forest Service, Great Lakes Forestry 

Centre; 34 p.

Schmid, J. M.; Bennett, D.; Young, R. W.; Mata, S.; Andrews, M.; Mitchell, J. 1982. Sampling larval populations of 

the pandora moth. Res. Note RM-421. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station; 5 p.

Schwalbe, C. P. 1981. Disparlure-baited traps for survey and detection. In: Doane, C. C.; McManus, M. L. editors. 

The gypsy moth: research toward integrated pest management. Tech. Bull. 1584. Washington, DC: U.S. 

Department of Agriculture, Forest Service; 542-548. 

Sharov, A. A., Roberts, E.A.; Ravlin, F. W.; Liebhold, A. M. 1995. Spread of gypsy moth (Lepidoptera: Lymantriidae) 

in the central Appalachians: three methods for species boundary estimation. Environmental Entomology 

24: 1529-1538.

Shenefelt, R. D.; Liebig, H. .R.; Dosen, R. C. 1955. Protecting machine transplanted trees from white grubs. Tree 

Planters’ Notes 20: 14-17.
256

Shepherd, R. F. 1985. Pest management of Douglas-fir tussock moth: estimating larval density by sequential 

sampling. Canadian Entomologist 117: 1111-1115.

Shepherd, R. F.; Brown, C. E. 1971. Sequential egg-band sampling and probability methods of predicting defoliation 

by Malacosoma disstria (Lepidoptera: Lasiocampidae). Canadian Entomologist 103: 1371-1379.

Shepherd, R. F.; Gray, T. G.; Chorney, R. J.; Daterman, G. E. 1985. Pest management of Douglas-fir tussock moth, 

Orgyia pseudotsugata (Lepidoptera: Lymantriidae): monitoring endemic populations with pheromone traps to 

detect incipient outbreaks. Canadian Entomologist 117: 839-847.

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.

Shepherd, R. F.; Otvos, I. S.; Chorney, R. J. 1985. Sequential sampling for Douglas-fir tussock moth egg masses in 

British Columbia. Joint Rep. 15. Canadian Forest Service, Pacific Forest Research Centre. 7 p. 

Shore, T. L. 1989. Sampling western hemlock looper pupae (Lepidoptera: Geometridae) using burlap traps. Journal 

of Entomological Science 24: 348-354.

Shore, T. L.; Alfaro, R. I.; Harris, J. W. E 1988. Comparison of binocular and cut-branch methods for estimating 

budworm defoliation of Douglas-fir. Journal of the Entomological Society of British Columbia 85: 15-20.

Sidebottom, J. R. 1995. Rust mites in Christmas trees. Christmas Tree Notes. Raleigh, NC: N.C. Agricultural 

Extension Service, North Carolina State University; 2 p.

Sidebottom, J. R. 1995. The spruce spider mite in Fraser fir. Christmas Tree Notes. Raleigh, NC: N.C. Agricultural 

Extension Service, North Carolina State University; 4 p.

Silk, P. J.; Kuenen, L. P. S.; Tan, S. H.; Roelofs, W. L.; Saunders, C. J.; Alford, A. R. 1985. Identification of sex 

pheromone components of the jack pine budworm, Choristoneura pinus pinus Freeman. Journal of Chemical 

Ecology 7: 159-167.

Simmons, G. A.; Fowler, G. W. 1982. Spruce budworm egg mass density on balsam fir and white spruce: low 

population levels. Great Lakes Entomology 15: 287-296.

Simmons, G. A.; Fowler, G. W. 1984. Considerations when sampling spruce budworm egg masses on balsam fir in 

the Lake states: low to extreme population levels. Great Lakes Entomology 17: 87-95.

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

York: Chapman & Hall Corp.; 524 p.

Sower, L. L.; Daterman, G. E.; Sartwell, C. 1984. Surveying populations of western pine shoot borers (Lepidoptera: 

Oleuthreutidae). Journal of Economic Entomology 77: 715-719.

Srivastava, N.; Campbell, R. W.; Torgersen, T. R.; Beckwith, R. C. 1984. Sampling the western spruce budworm: 

fourth instars, pupae, and egg masses. Forest Science 30: 883-892.

Stark, R. W. 1952. Sequential sampling of the lodgepole needleminer, Evagora milleri. Journal of Economic 

Entomology 55: 491-494.

Stark, R. W. 1952. Sequential sampling of the lodgepole needle miner. Forest Chronicle 28: 57-60.

Stephen, F. M.; Taha, H. A. 1976. Optimization of sampling effort for within-tree populations of southern pine 

beetle and its natural enemies. Environmental Entomology 5: 1001-1007.

Stephen, F. M.; Taha, H. A. 1979. Area-wide estimation of southern pine beetle populations. Environmental 

Entomology 8: 850-855.
257

Stevens, R. E.; Stark, R. W. 1962. Sequential sampling for the lodgepole needle miner, Evagora milleri. Journal of 

Economic Entomology 55: 491-494.

Stoszek, K. J. 1973. Damage to ponderosa pine plantations by the western pine shoot borer. Journal of Forestry 71: 

701-705.

Strickland, A. H. 1961. Sampling crop pests and their hosts. Annual Review of Entomology 6: 201-220.

Sweeney, J. D. 1998. Sequential sampling of spruce conelets to predict the category of seed loss due to spruce 

cone maggots. Unpublished Report, Canadian Forest Service, Natural Resources Canada, P.O. Box 4000, 

Fredericton, NB. Canada, E3B 5P7.

Sweeney, J. D.; Miller, G. F. 1989. Distribution of Barbara colfaxiana (Lepidoptera: Tortricidae) eggs within and 

among Douglas-fir crowns and methods for estimating egg densities. Canadian Entomologist 121: 569-

578.

Sweeney, J. D.; Miller, G. E.; Ruth, D. S. 1990. Sampling seed and cone insects in spruce. In: West, R. J., editor. 

Proceedings of the cone and seed pest workshop. 1989 October; St. John’s, Newfoundland. Inf. Rep. N-X274. Canadian Forest Service; 63-75.

Talerico, R. L.; Wilson, R. W., Jr. 1978. A sampling device for counting insect egg clusters and measuring vertical 

distribution of vegetation. Research Note NE-250. Broomall, PA: U.S. Department of Agriculture, Forest 

Service, Northeastern Forest Experiment Station; 4 p.

Thatcher, R. C.; Mason, G. N.; Hertel, G. D.; Searcy, J. L. 1982. Detecting and controlling the southern pine beetle. 

Southern Journal of Applied Forestry 6: 153-159.

Thorpe, K. W.; Ridgway, R. L. 1992. Gypsy moth (Lepidoptera: Lymantriidae) egg mass distribution and sampling in 

a residential setting. Environmental Entomology 21: 722-730.

Tilles, D. A.; Sjodin, K.; Nordlander, G.; Eidman, H. H.. 1986b. Synergism between ethanol and conifer host 

volatiles as attractants for the pine weevil, Hylobius abietis (L. ) (Coleoptera: Curculionidae). Journal of 

Economic Entomology 79: 970-973.

Tobi, D. R.; Leonard, J. G.; Parker B. L.; Wallner, W. E. 1992. Survey methods, distribution, and seasonality 

of Korshceltellus gracilis (Lepidoptera: Hepialidae) in the Green Mountains, Vermont. Environmental 

Entomology 21: 447-452.

Tostowaryk, W.; McLeod, J. M. 1972. Sequential sampling for egg clusters of the Swaine jack pine sawfly, Neodiprion 

swainei (Hymenoptera: Diprionidae). Canadian Entomologist 104: 1343-1347.

Turgeon, J. J.; Régnière, J. 1987. Development of sampling techniques for the spruce budmoth, Zeiraphera canadensis

Mut. and Free. (Lepidoptera: Tortricidae). Canadian Entomologist 119: 239-249.

Twardus, D. B. 1985. Surveys and sampling methods for population and damage assessment. In: Brookes, M. H.; 

Bolbert, J. J.; Mitchell, R. G.; Stark, R. W., editors. Managing trees and stands susceptible to western spruce 

budworm. Tech. Bull. 1695. Washington, DC: U.S. Department of Agriculture, Forest Service; 27-40. 

Wallner, W. E.; Jones, C. G.; Elkinton, J. S.; Parker, B. L. 1991. Sampling low density gypsy moth populations. In: 

Gottschalk, K. W.; Twery, M. J.; Fields, S. I., editors. Proceedings of the U.S. Department of Agriculture 

Interagency Gypsy Moth Research Review—1990. Gen. Tech. Rep. NE-146. Radnor, PA: U.S. Department 

of Agriculture, Forest Service, Northeastern Forest Experiment Station; 40-44.

Waters, W. E. 1955. Sequential sampling in forest insect surveys. Forest Science 1: 68-79.

Waters, W. E. 1974. Sequential sampling applied to forest insect surveys. In: Proceedings of IUFRO/SAF/SUNY 

symposium on monitoring forest environments through successive sampling. June 24-26; Syracuse, NY; 

290-311.
258

Waters, W. E.; Heller, R. C.; Bean, J. L. 1958. Aerial appraisal of damage by the spruce budworm. Journal of 

Forestry 56: 269-276.

Wickman, B. E. 1988. Seasonal variation of degree-day accumulation in relation to phenology of western spruce 

budworm, Douglas-fir tussock moth, and host trees in northeastern Oregon. Res. Note PNW-482. 

Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station; 11 p. 

Wilson, L. F. 1959. Branch “tip” sampling for determining abundance of spruce budworm egg masses. Journal of 

Economic Entomology 52: 618-621.

Wilson, L. F. 1966. Effects of different population levels of the European pine sawfly on young Scots pine trees. 

Journal of Economic Entomology 59: 1043-1049.

Wilson, L. F. 1987. Saratoga spittlebug—its ecology and management. Agric. Handb. No. 657. Washington, DC: U.S. 

Department of Agriculture, Forest Service; 56 p.

Wilson, L. F.; Gerrard, G. J. 1971. A new procedure for rapidly estimating European pine sawfly (Hymenoptera: 

Diprionidae) population levels in young pine plantations. Canadian Entomologist 103: 1315-1322.

Wilson, L. F.; Millers, I. 1983. Pine root collar weevil—its ecology and management. Tech. Bull. No. 1675. 

Washington, DC: U.S. Department of Agriculture, Forest Service; 34 p.

Wilson, L. F.; Wilkinson, R. C., Jr.; Averill, R. C. 1992. Redheaded pine sawfly—its ecology and management. Agric. 

Handbook 694. East Lakewood, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Region; 54 p.

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.
PERMISSIONS ACKNOWLEDGMENTS

259

Permissions Acknowledgments

Sources for each figure or table are indicated as they occurred in the text at the request of the original 

publishers. We wish to take this opportunity to express our gratitude to the following sources for granting 

permission to reprint previously published materials:

•	 California Agriculture, copyrighted by the University of California Regents, 1111 Franklin Street, 8th

floor, Oakland, CA 94607-5200

•	 Canadian Entomologist, copyrighted by the Entomological Society of Canada, 393 Winston 

Avenue, Ottawa, Canada K2A 1Y8

•	 Canadian Forest Service, Natural Resources Canada, Ottawa, Canada K1A 0E4

•	 Canadian Journal of Forest Research, copyrighted by the Canadian Institute of Forestry, 606-151 

Slater Street, Ottawa, Canada K1P 5H3

•	 Environmental Entomology, copyrighted by the Entomological Society of America, 9301 Annapolis 

Road, Lanham, MD 20706.

•	 Forest Science, copyrighted by the Society of American Foresters, 5400 Grosvenor Lane, Bethesda, 

MD 20814-2198 

•	 Journal of Economic Entomology, copyrighted by the Entomological Society of America, 9301 

Annapolis Road, Lanham, MD 20706

•	 Journal of Entomological Science, copyrighted by the Georgia Entomological Society, Inc., Georgia 

Experiment Station, Griffin, GA 30223-1797

•	 The New York State Museum, Albany, NY 12230

•	 Southern Journal of Applied Forestry, copyrighted by the Society of American Foresters, 5400 

Grosvenor Lane, Bethesda, MD 20814-2198 

•	 The Forest Chronicle, copyrighted by the Canadian Institute of Forestry, 606-151 Slater Street, 

Ottawa, Canada K1P 5H3

•	 University of Maine, Orono, ME 04469

•	 Virginia Polytechnic Institute and State University, Blacksburg, VA 24061

INDEX

261

Index

A

ABS pipe oviposition trap method 71

Acari: Tetranychidae

Hemlock rust mite, Nalepella tsugifolia Keifer 183

control decision-making, scouting program 183

Spruce spider mite, Oligonychus ununguis (Jacobi) 184

control decision-making, sequential sampling plan 184

Adelges abietis (L.)

See Homoptera: Adelgidae - Eastern spruce gall adelgid

Adelges cooleyi (Gillette)

See Homoptera: Adelgidae - Cooley spruce gall adelgid

Adelges piceae (Ratzeburg)

See Homoptera: Adelgidae - Balsam woolly adelgid

adult survey 5, 7, 8, 9, 11, 12, 15, 16, 17, 23, 25, 105, 124, 135, 147, 182, 220, 223, 225, 227, 229, 236, 

239, 242, 244

damage prediction 9, 11, 17

forecasting seedling damage, pit trap method 5

pheromone-baited traps 25, 105, 147, 124, 135, 181

PVC pitfall trap method 7, 8, 9, 11, 15, 16, 17

spatial and temporal distributions on log sorts 244

sticky trap and Malaise trap methods 25, 147

within-tree populations 236

aerial detection, review of 237

Allen, D. C.; Abrahamson, L. P.; Eggen, D. A.; Lanier, G. N., Swier, S. R., Kelley, R. S.; Auger, M. 135

Allen, D. C.; Dorais, L.; Kettela, E. G. 133

Amman, G. D. 188

Anisota senatoria (J.E. Smith)

See Lepidoptera: Saturniidae - Orangestriped oakworm

Aphrophora saratogensis (Fitch)

See Homoptera: Cercopidae - Saratoga spittlebug

Arkansas SPB Model 223

Ashraf, M. 243

B

baited pitfall traps 5, 7, 8, 9, 11, 12, 15, 16, 17

Balch, R. E.; Underwood, G. G. 190

Balsam twig aphid

See Homoptera: Aphididae

Balsam woolly adelgid

See Homoptera: Adelgidae

Barbara colfaxiana (Kearfott)
262

See Lepidoptera: Tortricidae - Douglas-fir cone moth

Barbosa, P.; Wagner, M. R. 1, 2

Batzer, H. O.; Jennings, D. T. 176

Batzer, H. O.; Martin, M. P.; Mattson, W. J.; Miller, W. E. 72

Benjamin, D. M. 47

Berryman, A. A. 242, 243

Billings, R. F. 1, 2, 220

Billings, R. F.; Doggett, C. 232

Billings, R. F.; Pase, H. A. III 232

Binns, M. R.; Bostanian, N. J. 102

Brown, L. E. 245

Bruce spanworm 

See Lepidoptera: Geometridae

bud, shoot, and root insects 5-41

See also Coleoptera: Curculionidae

Coleoptera: Scarabiidae

Lepidoptera: Hepialidae

Lepidoptera: Tortricidae

Buss, L. J.; McCullough, D. G.; Ramm, C. W. 80, 96

C

Carlson, R. W.; Cole, W. E. 232

Carolin, V. M.; Coulter, W. K. 157

Carolin, V. M.; Johnson, N. E.; Buffam, P. E.; McComb, D. 66

Carolin, V. M., Jr.; Honing, F. W. 158

Carter, J. L.; Ravlin, F. W. 83

Carter, J. L.; Ravlin, F. W.; Gray, D. R.; Carter, M. R.; Coakley, C. W. 82

Ciesla, W. M.; Bousfield, W. E. 57

Choristoneura biennis Freeman

See Lepidoptera: Tortricidae - Two-year cycle spruce budworm

Choristoneura fumiferana (Clemens)

See Lepidoptera: Tortricidae - Spruce budworm

Choristoneura occidentalis Freeman

See Lepidoptera: Tortricidae - Western spruce budworm

Choristoneura pinus Freeman

See Lepidoptera: Tortricidae - Jack pine budworm

Citricola scale

See Homoptera: Coccidae

Coccus pseudomagnolarium (Kuwana)

See Homoptera: Coccidae - Citricola scale

Coffelt, M. A.; Schultz, P. B. 1, 2, 128, 129

Cole, W. E. 159

Coleophora laricella (Hübner)

See Lepidoptera: Coleophoridae - Larch casebearer

Coleoptera: Cerambycidae

Northeastern sawyer, Monochamus notatus (Drury) 215

Oregon fir sawyer, Monochamus scutellatus oregonensis (LeConte) 217

Coleoptera: Chrysomelidae

Elm leaf beetle, Pyrrhalta luteola (Müller) 43

Coleoptera: Curculionidae

Pales weevil, Hylobius pales (Herbst) 5
263

damage prediction, adult survey 9

forecasting seedling damage 5

pit trap method 5

PVC pitfall trap method 7, 8, 9

Pine root collar weevil, Hylobius radicis Buchanan 11

damage appraisal survey 13

damage prediction, adult survey 11

hazard rating systems, comprehensive review of 12

survey techniques, comprehensive review of 12

Pitcheating weevil, Pachylobius picivorus (Germar) 15

damage prediction, adult survey 17

PVC pitfall trap method 15, 16, 17

Coleoptera: Scarabiidae

White grubs, Pyllophaga spp. 19

Coleoptera: Scolytidae

Fir engraver, Scolytus ventralis LeConte 242

Mountain pine beetle, Dendroctonus ponderosae Hopkins 231

within-tree density, optimum sampling sizes, estimation of 236

within-tree populations, sequential sampling plan, adult survey 231

Southern pine beetle, Dendroctonus frontalis Zimmerman 220

aerial detection, review of 233

Arkansas SPBMODEL 223

attacking adult densities, estimation of 225

ground checking, review of 233

hazard rating, review of 231

hog model 223

pheromone-baited traps 220

predicting spot growth 223

regional infestation trends, forecasting 220

stand level population density, estimation of 229

within-tree population density, estimation of 227

Spruce beetle, Dendroctonus rufipennis Kirby 239

Striped ambrosia beetle, Trypodendron lineatum (Olivier) 244

Western pine beetle, Dendroctonus brevicomis LeConte 218

Coleotechnites milleri (Busck)

See Lepidoptera: Gelechiidae - Lodgepole needle miner

Coloradia pandora Blake

See Lepidoptera: Saturniidae - Pandora moth

Conifer swift moth

See Lepidoptera: Hepialidae

Connola, D. P.; Waters, W. E.; Nason, E. R. 49

Connola, D. P.; Waters, W. E.; Smith, W. E. 73

Contarinia oregonensis Foote

See Diptera: Cecidomyiidae - Douglas fir cone gall midge

control decision-making 43, 124, 128, 183, 184, 194, 195, 196, 199, 201, 202, 211

beat disc and visual count surveys 195

crude decision-making 195, 196, 201, 211 

defoliation surveys 128

egg and larval survey 201, 202, 211

population monitoring 194, 196, 199

scouting program 183
264

sequential sampling plan 184

survey methods, review of 196

Cooley spruce gall adelgid

See Homoptera: Adelgidae

Coulson, R. N.; Pulley, P. E.; Foltz, J. L.; Martin, W. R. 230

Coulson, R. N.; Witter, J. A. 1, 2

D

Dahlsten, D. L.; Rowney, D. L.; Lawson, A. B. 43

Daterman, G. E. 26, 104

defoliating insects 43-181

See also Coleoptera: Chrysomelidae

Hymenoptera: Diprionidae

Lepidoptera: Coleophoridae

Lepidoptera: Gelechiidae

Lepidoptera: Geometridae

Lepidoptera: Lasiocampidae

Lepidoptera: Lymantriidae

Lepidoptera: Notodontidae

Lepidoptera: Saturnidae

Lepidoptera: Tortricidae

Dendroctonus brevicomis LeConte

See Coleoptera: Scolytidae - Western pine beetle

Dendroctonus frontalis Zimmerman

See Coleoptera: Scolytidae - Southern pine beetle

Dendroctonus ponderosae Hopkins

See Coleoptera: Scolytidae - Mountain pine beetle

Dendroctonus rufipennis Kirby

See Coleoptera: Scolytidae - Spruce beetle

detection survey

European pine shoot moth 26

Pine root collar weevil 12

Redheaded pine sawfly 45

Dimond, J. B. 189

Diptera: Anthomyiidae

Spruce cone maggot, Strobilomyia neanthracina Michelsen 201

control decision-making, egg and larval survey 202

crude decision-making, egg and larval survey 201

Diptera: Cecidomyiidae

Douglas-fir cone gall midge, Contarinia oregonensis Foote 204

egg deposition distribution, recommended sample sizes, egg survey 204

fixed and sequential sampling plans, tree and orchard level 205

Dobesberger, E. J. 63

Dobesberger, E. J.; Lim, K. P. 136

Douglas-fir cone gall midge

See Diptera: Cecidomyiidae

Douglas-fir cone moth

See Lepidoptera: Tortricidae

Douglas-fir tussock moth

See Lepidoptera: Lymantriidae
265

Dreistadt, S. H. ; Flint, M. L. 194, 199

Drepanaphis acerifoliae (Thomas)

 See Homoptera: Aphididae - Painted maple aphid

Dudley, C. O. 218

E

Eastern spruce gall adelgid

See Homoptera: Adelgidae

Eastern hemlock looper 

See Lepidoptera: Geometridae

egg and egg mass sampling 39, 43,46, 49, 51, 53, 55, 63, 65, 66, 67, 71, 72, 73, 75, 80, 82, 84, 85, 87, 91, 92, 96, 

99, 102, 104, 105, 107, 110, 119, 120, 124, 126, 129, 132, 137, 138, 151, 154, 162, 165, 170, 172, 176, 201, 204, 

209, 211, 218

ABS pipe oviposition method 71

adjusting winter estimates 81

branch “tip” estimates 154

burlap band method 104

control decision-making 43, 194, 202, 211

crude defoliation predictions 124

damage prediction 165

egg deposition distribution, recommended sample sizes 204

egg distribution within trees 137, 162

fixed- and variable-radius plot methods 83, 105

mid-crown estimates 151

100-m2

 plots to predict defoliation levels 96

100-tree plot method 80

precision levels 67, 92, 110, 129

sequential sampling forested and suburban areas 86

sequential sampling plan 49, 53, 55, 63, 73, 75, 107, 119, 126, 138, 209

sequential sampling plan and fixed-radius plots 85

suburban environments 102

use of vertical sampling pole 51

egg distribution within trees 66, 204

egg mass and larval survey 107, 110, 126, 129, 157

regression equations 157 

sampling with known precision 110, 129

sequential sampling plan 107, 126

Elm leaf beetle

See Coleoptera: Chrysomelidae

ethanol and turpentine in pitfall traps 7, 8, 9, 11, 15, 16, 17

Eucosma sonomana Kearfott

See Lepidoptera: Tortricidae - Western pine shoot borer

European pine sawfly

See Homoptera: Diprionidae

European pine shoot moth

See Lepidoptera: Tortricidae

F

Fatzinger, C. W. 213

Fatzinger, C. W.; Dixon, W. N. 213
266

Fatzinger, C. W.; Muse, H. D.; Miller, T.; Bhattacharyya, H. T. 212

Fettes, J. J. 148

Fettig, C. J.; Berisford, C. W. 28

Fettig, C. J.; Salom, S. M. 5

Fidgen, J. G.; Teerling, C. R.; McKinnon, M. L. 186

Fir engraver

See Coleoptera: Scolytidae

fixed- and variable-radius plots 83, 91, 95, 104

Fleischer, S. J.; Ravlin, F. W.; Reardon, R. C. 84

Fleischer, S.; Carter, J.; Reardon, R.; Ravlin F. 1, 2, 86

Foltz, J. L.; Knight, F. B.; Allen, D. C.; Mattson, W. J., Jr. 177

Foltz, J. L.; Mayyasi, P. M.; Pulley, P. E.; Coulson, R. N.; Martin, C. W. 230

forecasting of regional infestation trends 220

Forest tent caterpillar

See Lepidoptera: Lasiocampidae

FORTRAN 29, 30, 36, 37

Fowler, G. W.; Simmons, G. A. 137, 151

frass measurements 92

G

gall survey 186

Gansner, D. A.; Herrick, O. W.; Ticehurst, M. 90

Gargiullo, P. M.; Berisford, C. W. 29, 31, 36, 38

Gargiullo, P. M.; Berisford, C. W.; Pienaar, L. V. 1, 30, 37

Grimble, D. G.; Kasile, J. D. 126

Gypsy moth 80, 82, 84, 85, 87, 91, 92, 94, 96, 99, 100, 102, 104, 105

See also Lepidoptera: Lymantriidae

H

Hard, J. S. 55

Harris, J. W. E. 132, 162

Hemlock rust mite 183

See also Acari: Tetranychidae

Herbert, C.; St-Antoine, L. 71

Heterocampa guttvita (Walker)

See Lepidoptera: Notodontidae - Saddled prominent

Homoptera: Adelgidae

Balsam woolly adelgid, Adelgis piceae (Ratzeburg) 188

Cooley spruce gall adelgid, Adelges cooleyi (Gillette) 187

Eastern spruce gall adelgid, Adelges abietis (L.) 186

Pine leaf adelgid, Pineus pinifolia (Fitch) 189

Homoptera: Aphididae

Balsam twig aphid, Mindarus abietinus Koch 195

Painted maple aphid, Drepanaphis acerifoliae (Thomas) 194

Homoptera: Cercopidae

Saratoga spittlebug, Aphrophora saratogensis (Fitch) 196

Homoptera: Coccidae

Citricola scale, Coccus pseudomagnolarium (Kuwana) 199

Hunt, D. W. A.; Raffa, K. F. 7, 11, 15, 16

Hylobius pales (Herbst)
267

See Coleoptera: Curculionidae

Hylobius radicis Buchanan

See Coleoptera: Curculionidae

Hymenoptera: Diprionidae

European sawfly, Neodiprion sentifer (Geoffroy) 51

use of vertical sampling to determine egg density, egg survey 51

regression equation relating damage to sawfly density, larval survey survey 52

Hemlock sawfly, Neodiprion tsugae Middleton 55

Red pine sawfly, Neodiprion nanulus nanulus Schedl. 45, 49

Redheaded pine sawfly, Neodiprion lecontei (Fitch) 45

Swaine jack pine sawfly, Neodiprion swainei Middleton 53

I

Interception trap study 23

Ives, W. G. H.; Warren, G. L. 19

J

Jack pine budworm, Choristoneura pinus Freeman 176

See also Lepidoptera: Tortricidae

Jobin, L. J.; Coulombe, C. 71

K

Korshceltellus gracilis

See Lepidoptera: Hepialidae - Conifer swift moth

Kleintjes, P. K.; Lemoine, E. E; Schroeder, J.; Solensky, M. J. 195

Knight, F. B. 236, 239

Kolodny-Hirsch, D. M. 92

Kulman, H. M.; Hodson, A. C. 177

Kuno, E. 1

L

Lambdina fiscellaria fiscellaria (Guenée)

See Lepidoptera: Geometridae - Eastern hemlock looper

Lambdina fiscellaria lugubrosa (Hulst)

See Lepidoptera: Geometridae - Western hemlock looper

Lance, D. R.; Elkinton, J. S.; Scwalbe, C. P. 93

larval sampling 12, 13, 19, 29, 30, 36, 37, 39, 46, 52, 57, 59, 61, 106, 107, 110, 112, 115, 117, 118, 122, 126, 129, 131, 

134, 136, 137, 139, 141, 152, 157, 159, 163, 169, 172, 176, 177, 178, 201, 202, 211, 218, 227, 230, 234

absolute population estimates 29, 36

FORTRAN program relating damage to sawfly density 52

low density population 110

precision 109, 131, 136, 136, 139, 163, 178, 218, 227, 234

sequential sampling plan 19, 39, 59, 106, 114, 121, 126, 129, 152, 159

Lasota, J. A.; Shetlar, D. J. 187

Lepidoptera: Coleophoridae

Larch casebearer, Coleophora laricella (Hübner) 57

Lepidoptera: Gelechiidae

Lodgepole needle miner, Coleotechnites milleri (Busck) 59

sequential sampling plan, larvae 59, 61
268

sequential sampling plan, pupae 61

Lepidoptera: Geometridae

Bruce spanworm, Operophtera bruceata (Hulst) 71

Eastern hemlock looper, Lambdina fiscellaria fiscellaria (Guenée) 63

processing moss and bark samples for egg sampling, method for 65

sequential sampling plan, egg survey 63

Western hemlock looper, Lambdina fiscellaria lugubrosa (Hulst) 66

burlap band method, pupal sampling with known precision 69

egg sampling 66, 67

Lepidoptera: Hepialidae 

Conifer swift moth, Korscheltellus gracilis (Grote) 23

Lepidoptera: Lasiocampidae

Forest tent caterpillar, Malacosoma disstria (Hübner) 72

regression equations for density, egg mass sampling 72

sequential sampling plan, egg survey 73

sequential sampling plan adjusted for stage of outbreak, egg survey 75

Lepidoptera: Lymantriidae

Douglas-fir tussock moth, Orygia pseudotsugata (McDunnough) 106

crude defoliation predictions, egg mass survey 124

general sampling methods, comprehensive review of 106

larval sampling methods, comprehensive review of 117

low density populations, regression equations, larval survey 112

lower crown samples, mid-crown population estimates from 118

pheromone-baited traps, adult survey 123

sampling with known precision, egg and larval 110

sequential sampling plan, egg and larval survey 107

sequential sampling plan, egg mass survey 120

sequential sampling plan, larval survey 115, 122

Gypsy moth, Lymantria dispar (L.) 80

adjusting winter estimates, egg mass survey 82

burlap band method, egg mass survey 104

constant-precision levels, egg mass survey 92

damage prediction 10 km from site, egg mass and pupal survey 99

defoliation prediction in 1 hectare grids, regression equations for 100

fixed- and variable-radius plot methods, egg mass survey 84, 105

100-m2

 plot to predict defoliation levels, egg mass survey 91, 96

100-tree plot method, egg mass survey 80

pellet numbers and size, frass measurement 94

sequential sampling forested and suburban areas, egg mass survey 87

sequential sampling plan and fixed-radius plots, egg mass survey 85

suburban environments, egg mass survey 102

Lepidoptera: Notodontidae

Saddled prominent, Heterocampa guttivita (Walker) 126

Lepidoptera: Saturniidae

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

control decision-making, defoliation surveys 128

known precision, egg and larval surveys with 129

Pandora moth, Coloradia pandora Blake 131

Lepidoptera: Tortricidae

Douglas-fir cone moth, Barbara colfaxiana (Kearfott) 209
269

European pine shoot moth, Rhyacionia buoliana (Dennis & Schiff.) 27

Jack pine budworm, Choristoneura pinus Freeman 176

egg mass density, stand density influences on 176

optimum sample sizes, larval survey 179

pheromone-baited trap method, adult survey 182

regression equations, larval survey 178, 179

regression equations, larval and pupal survey 177

Nantucket pine tip moth, Rhyacionia frustrana (Comstock) 28

absolute population estimates, larval survey 29

regression equations 28

sampling with known precision 30

sequential sampling 32

visual field damage estimates 28

Pitch pine tip moth, Rhyacionia rigidana (Fernald) 36, 37

Spruce budmoth, Zeiraphera canadensis Mut. & Free. 39, 40

Spruce budworm, Choristoneura fumiferana (Clemens) 133

branch “tip” estimates, egg mass survey 154

egg deposition distributions within trees, egg mass survey 137

mid-crown estimates, egg mass survey 151

optimum sample sizes, larval survey with known precision 136, 139

pheromone-baited trap method, adult survey 135

sampling methods for all life stages, hazard rating, review of 142

sequential sampling plan, larval survey 152

sequential sampling plans, egg mass survey 137

survey, impact and hazard rating methods, review of 133

Spruce seed moth, Cydia strobilella (L.) 211

Two-year cycle spruce budworm, Choristoneura biennis Freeman 132

Western pine shoot borer, Eucosma sonomana Kearfott 25

Western spruce budworm, Choristoneura occidentalis Freeman 157

damage prediction, egg mass survey, sequential sampling plan 165

defoliation levels, visual and cut-branch methods of classifying 169

egg deposition distributions within trees, egg mass survey 162

non-destructive sampling, larval survey with known precision 163

regression equations, egg mass and larval sampling 157

regression equations, egg mass, larval and pupal surveys 170

sampling methods for all life stages, review of 172

sequential sampling plan, larval sampling 159

Liang, Q.; Otvos, I. S.; Bradfield, G. E. 67

Liebhold, A. M.; Elkinton, J. S. 92

Liebhold, A. M.; Elkinton, J. S.; Zhou, G.; Hohn, M. E.; Rossi, R. E.; Boettner, G. H.; Boettner, C. W.; Burnham C.; 

McManus, M. L. 99

Liebhold, A.M.; Luzader, E.; Reardon, R.; Roberts, A.; Ravlin, F. W.; Sharov, A.; Zhou, G. 100

Liebhold, A. M.; Thorpe, K.; Ghent, J.; Lyons, D. B. 96, 101

Lih, M. P.; Stephen, F. M. 223

Lindgren, B. S.; Borden, J. H. 244

Linit, M. J.; Stephen, F. M. 225

Lodgepole needle miner, Coleotechnites milleri (Busck) 50

See also Lepidoptera: Gelechiidae

Lynch A. M.; Fowler, G. W.; Simmons, G. A. 138

Lyons, L. A. 51
270

M

Malacosoma disstria (Hübner)

See Lepidoptera: Lasiocampidae - Forest tent caterpillar

Malaise traps 24

Mangini, A.; Carlton, C.; Perry, R. W.; Hanula, J. 16

Mason, R. R. 1, 107, 110, 112, 113, 114, 115, 117, 118

Mason, R. R.; Wickman, B. E; Paul, H. G. 163

McGraw, J. R.; Hain, F. P. 1

McKnight, M. E.; Chansler, J. F.; Cahill, D. B.; Flake, H. W., Jr. 165

Miller, C. A. 148

Miller, C. A.; Ketella, E. G. 148

Miller, C. A.; Ketella, E. G. ; McDougall, C. A. 148

Miller, C. A.; Greenbank, D. O.; Ketella, E. G. 148

Miller, G. E. 204, 205

Miller, W. E. 27, 29

Miller, W. E.; Wilson, L. F. 31, 36, 38

Mindarus abietinus Koch

See Homoptera: Aphididae - Balsam twig aphid

Monochamus notatus (Drury)

See Coleoptera: Cerambycidae - Northeastern sawyer

Monochamus scutellatus oregonensis (LeConte)

See Coleoptera: Cerambycidae - Oregon fir sawyer

Mountain pine beetle, Dendroctonus ponderosae Hopkins

See Coleoptera: Scolytidae

N

Nalepella tsugifolia Keifer

See Acari: Tetranichidae

Nantucket pine tip moth 

See Lepidoptera: Tortricidae 

Nealis, V. G.; Lysyk, T. J. 178

Neodiprion lecontei (Fitch)

See Hymenoptera: Diprionidae - Redheaded pine sawfly

Neodiprion nanulus nanulus Schedl.

See Hymenoptera: Diprionidae - Red pine sawfly

Neodiprion sertifer (Geoffroy)

See Hymenoptera: Diprionidae - European pine sawfly

Neodiprion swainei Middleton 

See Hymenoptera: Diprionidae - Swaine jack pine sawfly

Neodiprion tsugae Middleton

See Hymenoptera: Diprionidae - Hemlock sawfly

Northeastern sawyer, Monochamus notatus (Drury) 

See Coleoptera: Cerambycidae 

O

Operophtera bruceata (Hulst)

See Lepidoptera: Geometridae - Bruce spanworm

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

See Lepidoptera: Saturniidae

Oregon fir sawyer, Monochamus scutellatus oregonensis (LeConte) 
271

See Coleoptera: Cerambycidae

Orygia pseudotsugata (McDunnough) 

See Lepidoptera: Lymantriidae - Douglas-fir tussock moth

Otvos, I. S.; Bryant, D. G. 65 P

Pachylobius picivorus (Germar)

See Coleoptera: Curculionidae - Pitcheating weevil

Pandora moth, Coloradia pandora Blake

See Lepidoptera: Saturniidae

Painted maple aphid, Drepanaphis acerifoliae (Thomas)

See Homoptera: Adelgidae

Pales weevil, Hylobius pales (Herbst)

See Coleoptera: Curculiomidae

Pedigo, L. P.; Buntin, G. D. 1

Pendrel, B. A. 182

pheromone-baited traps 25, 122, 135, 180

Phyllophaga spp.

See Coleoptera: Scarabiidae

piercing and sucking insects and mites 183-199

See also Acari: Tetranychidae

Homoptera: Adelgidae

Homoptera: Aphididae

Homoptera: Cercopidae

Homoptera: Coccidae

Pine leaf adelgid, Pineus pinifolia (Fitch) 

See Homoptera: Adelgidae

Pine root collar weevil, Hylobius radicis Buchanan

See Coleoptera: Curculionidae

Pineus pinifolia (Ratzeburg)

See Homoptera: Adelgidae - Pine leaf adelgid

Pitch pine tip moth, Rhyacionia rigidana (Fernald)

See Lepidoptera: Tortricidae

Pitcheating weevil, Pachylobius picivorus (Germar)

See Coleoptera: Curculionidae

Preble, M. L. 147

pupae survey 13, 46, 61, 69, 145, 173, 177

PVC pitfall trap method 7, 8, 11, 15, 16, 17

Pyrrhalta luteola (Müller) 

See Coleoptera: Chrysomelidae - Elm leaf beetle

R

Raffa, K. F.; Hunt, D. W. A. 8

Ramaswamy, S. B.; Carde, B. T. 135

Red pine sawfly, Neodiprion nanulus nanulus Schedl. 

See Hymenoptera: Diprionidae

Redheaded pine sawfly

See Hymenoptera: Diprionidae

Régnière, J. J.; Sanders, C. J. 139
272

Rhyacionia buoliana (Dennis & Schiff.) 

See Lepidoptera: Tortricidae - European pine shoot moth

Rhyacionia frustrana (Comstock) 

See Lepidoptera: Tortricidae - Nantucket pine tip moth

Rhyacionia rigidana (Fernald) 

See Lepidoptera: Tortricidae - Pitch pine tip moth

Rieske, L. K.; Raffa, K. F. 6, 9, 17

S

Safranyik, L.; Raske, A. G. 215, 217

Sanders, C. J. 134, 140

Saratoga spittlebug, Aphrophora saratogensis (Fitch)

See Homoptera: Cercopidae

Schmid, J. M.; Bennett, D.; Young, R. W.; Mata, S.; Andrews, M.; Mitchell, J. 131

Schwalbe, C. P. 100

Scolytus ventralis LeConte

See Coleoptera: Scolytidae - Fir engraver

seed and cone insects 201-213

See also Diptera: Anthomyiidae

Diptera: Cecidomyiidae

Lepidoptera: Tortricidae

sequential sampling plan(s) 19, 29, 30, 32, 36, 39, 49, 52, 53, 55, 59, 61, 63, 73, 75, 96, 108, 113, 116, 118, 123, 126, 

129, 131, 136, 137, 152, 159, 163, 165, 175, 176, 184, 189, 201, 202, 205, 209, 215, 217, 236, 239

damage 32, 189, 202, 205, 215, 217

egg and egg mass 49, 53, 55, 63, 73, 75, 97, 99, 109, 119, 122, 124, 138, 165, 184, 209

larvae 19, 29, 36, 39, 52, 59, 61, 109, 114, 117, 120, 121, 126, 129, 131, 136, 152, 159, 163, 176, 177, 201, 202

Sharov, A. A.; Roberts, E. A.; Ravlin, F. W.; Liebhold, A. M. 101

Shenefelt, R. D.; Liebig, H. .R.; Dosen, R. C. 20

Shepherd, R. F. 120, 122

Shepherd, R. F.; Brown, C. E. 75

Shepherd, R. F.; Gray, T. G.; Chorney, R. J.; Daterman, G. E. 122, 124

Shepherd, R. F.; Otvos, I. S.; Chorney, R. J. 118, 120, 121, 123, 124, 124, 125

Shore, T. L. 67, 69

Shore, T. L.; Alfaro, R. I.; Harris, J. W. E 169

Sidebottom, J. R. 183, 184

Simmons, G. A.; Fowler, G. W. 151

Southern pine beetle, Dendroctonus frontalis Zimmerman

See Coleoptera: Scolytidae

Southwood, T. R. E. 1

Sower, L. L.; Daterman, G. E.; Sartwell, C. 25

Spruce

P. engelmanii L. 20

Picea glauca (Moensch) Voss. 201

Picea spp. 201

Srivastava, N.; Campbell, R. W.; Torgersen, T. R.; Beckwith, R. C. 169

Stark , R. W. 1, 59, 61

Stephen, F. M.; Taha, H. A. 227, 229

Stevens, R. E.; Stark, R. W. 61

Stoszek, K. J. 26
273

Strickland, A. H. 1

Strobilomyia neanthracina Michelsen

See Diptera: Anthomyiidae - Spruce cone maggot

Swaine jack pine sawfly, Neodiprion swainei Middleton 

See Hymentopera: Diprionidae

Sweeney, J. D. 202

Sweeney, J. D.; Miller, G. F. 209

Sweeney, J. D.; Miller, G. E.; Ruth, D. S. 201, 211

T

Talerico, R. L.; Wilson, R. W., Jr. 51

Thatcher, R. C.; Mason, G. N.; Hertel, G. D.; Searcy, J. L. 221, 230, 231

Thorpe, K. W.; Ridgway, R. L. 102

Tilles, D. A., Sjödin, K.; Nordlander, G.; Eidmann 8, 10, 18

Tobi, D. R.; Leonard, J. G.; Parker B. L.; Wallner 23

Tostowaryk, W.; McLeod, J. M. 53

Tryodendron lineatum (Oliver)

See Coleoptera: Scolytidae - Striped ambrosia beetle

Turgeon, J. J.; Régnière, J. 39

Twardus, D. B. 171

W

Wallner, W. E.; Jones, C. G.; Elkinton, J. S.; Parker, B. L. 104

Waters, W. E. 32, 152

Waters, W. E.; Heller, R. C.; Bean, J. L. 147

Western pine shoot borer

See Lepidoptera: Tortricidae

White grubs

See Coleoptera: Scarabiidae

Wilson, L. F. 32, 52, 154, 196

Wilson, L. F.; Gerrard, G. J. 52

Wilson, L. F.; Millers, I. 12

Wilson, L. F.; Wilkinson, R. C., Jr.; Averill, R. C. 45

Wilson, R. W. Jr.; Fontaine, G. A. 97, 105

wood- and bark-boring insects 215-244

See also Coleoptera: Cerambycidae

Coleoptera: Scolytidae

Z

Zeiraphera canadensis eggs 

See Lepidoptera: Tortricidae