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Predicting
Defoliation
by Gypsy Moth
the
in Connecticut
BY RONALD M. WESELOH
938
Bulletin
October 1996
ABSTRACT
A
procedure has been developed to predict forest defoliation by the gypsy moth, Ly-
mantria dispar L., in Connecticut using past defoliation, egg mass counts, and physiog-
raphic features to fit logistic regression models. In a validation study, the models pre-
dicted the amount of defoliation quite accurately, and when defoliation was substantial,
they also predicted the locations ofdefoliation accurately. However, when actual defolia-
tion was very low, locations where defoliation had been predicted did not often corre-
spond to actual areas ofdefoliation. The procedure should be useful in helping to make
control decisions, and an automated implementation of the method has been developed
and is described.
The Connecticut Agricultural Experiment Station is an Equal Opportunity, Affirmative
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Predicting Defoliation by the Gypsy Moth
in Connecticut
By Ronald M. Weseloh
Forthe last several years, gypsy moth, Lymantria dispar gression. Kriging is a geostatistical technique that is useful
L, population numbers have been low in Connecticut. This for interpolating between sample points. Results have the
appears to be due to the activity of a pathogenic fungus, lowest variance ofall interpolation techniques, and kriging
Entomophaga maimaiga Humber, Shimazu & Soper, that is especially useful when sample points are clumped. How-
was discovered in Connecticut in 1989 (Andreadis and We- ever, kriging requires knowledge ofthe spatial correlations
seloh 1990). However, the ecology ofthis pathogen is not in the data, and is computationally tedious (see Isaaks and
well enough known to predict with any certainty its impact Srivastava (1989) for further explanations of kriging and
on future gypsy moth numbers. Thus, the ability to predict other geostatistical techniques). For routine use, a simpler
forest defoliation by the gypsy moth in Connecticut is desir- procedure that can be easily automated would be desirable.
able to assistregulatory and management personnel. At pre- This bulletin describes such a procedure and presents data
sent, winter egg mass counts on an 11.25 km grid system on the method's ability to predict gypsy moth defoliation in
throughout Connecticut serve as rough guidelines for mu- Connecticut. An automated implementation ofthe process is
nicipalities, state, and federal agencies. Towns can also re- describedintheAppendix.
quest more detailed surveys. While the procedure provides
valuable guidance to municipalities andcitizens' groups that METHODS
are concerned with gypsy moth control, interpretations are
highly dependent on the experience ofthe predictor, andthe In this study, extensive use was made ofthe Geographi-
detailed surveys can be time consuming. A more objective cal Information System, IDRISI (Eastman 1993), for data
approach that relies exclusively on the statewide datawould andmap analysis.
be desirable and lessexpensive.
Fortunately, there has been considerable progress in de- Sources ofData
veloping objective procedures for gypsy moth defoliation U.S. Geological Survey Digital Elevation Model files
prediction, including the use ofnon-linear or linear regres- obtained from the Map Library of the University of Con-
sions (Gage et al. 1990, Gansner et al. 1985, Williams et al. necticutweremanipulatedusing IDRISItoproduce amap of
1991, Montgomery 1990, andLiebhold et al. 1993). Perhaps ground elevation above sea level at 2 km resolution for the
the potentially most useful approach involves geographical State of Connecticut. Also, a Connecticut map at the same
information systems (GIS) and geostatistics (Liebhold et al. resolution giving percent ofsoil areas having poor drainage
1991, Liebhold et al. 1993a, Liebhold et al. 1993b, Hohn et was constructed from data from the U.S. Department ofAg-
al. 1993, Gribko et al. 1995) and cellular transition models riculture Soil Conservation Service, National Cooperative
(Zhou and Liebhold 1995), that explicitly take into account Soil Survey, FortWorth, Texas.
the spatial structure ofthe data. Digitized maps of defoliation from 1969 to 1995 were
Weseloh (in press) developed a procedure similar to a obtained from aerial sketch maps ofdefoliation in Connecti-
method describedby Gribko etal. (1995)which includedthe cut obtained from personnel in the State Entomologist's of-
use ofphysiographic features and estimates ofprevious de- fice. Details ofthe digitizing process are given in Weseloh
foliation and eggmass countsto predict future defoliation in (in press). Defoliation was coded at 5 levels: = 0% to 9%,
Connecticut through application of a logistic regression 1 = 10%to25%, 2 = 26%to 50%, 3 = 51%to 75%, and4 =
model. This procedure was cumbersome, involving the use 76% to 100%, and map resolution was 2 km. An average
of kriging to interpolate egg mass counts, and having 10 defoliation map was generated from the yearly maps from
independent variables and 12 interactions in the logistic re- 1969 to 1994 by adding defoliation levels for each resolved
ConnecticutAgricultural ExperimentStation Bulletin 938
pointforall yearsandthen dividing bythenumberofyears. were made only for 1985 and 1989-1995 because of defi-
From 1975 through 1988 in Connecticut, regulatory per- ciencies in eggmassdata in otheryears.
sonnel carried out egg mass counts on 51 plots (0.023 ha) Comparisons between predictions and actual defoliation
distributed on a square, 16 km grid in oak-dominated areas weremadeby visual inspection andby aggregating values to
throughoutthe state. In these plots, all new egg masses seen 6 km resolution and calculating the correlation coefficient
on the trunks oftrees were counted. From 1989 to the pres- between actual andpredicted defoliationpairs foreach year.
ent, 102 plots distributed on a grid every 11.25 km have
been sampled. The number ofegg masses per acre (0.4 ha) RESULTS
was converted to the natural logarithm after 1 was added to
each value. To convert the transformed egg mass counts to Maps ofelevation and average defoliation from 1969 to
maps at 2 km resolution, values were interpolated using in- 1994 are presented in Fig. 1. Average defoliation is related
verse-distance weighting (Isaaks and Srivastava 1989), in to elevation over much ofConnecticut except in the south-
which the six closest sample points to the grid point for east, where persistent defoliation tends to occur. The char-
which the estimated valuewas desired were weightedby the acteristics leadingtothispersistence arenotknown.
inverse of the distance between the sample points and the Fig. 2 shows examples ofinterpolated eggmass numbers
estimated point. The weighted average of these sample for 1990 as determined by kriging in Weseloh (in press) or
pointswas usedasthe estimate. Thismethodofinterpolation inverseweighting as described in thispaper. Results are very
was used instead ofkriging because it is less computation- similar, confirming that when sample points are evenly
ally expensive and, because egg mass counts were distrib- spaced, inverse weighting is comparable to kriging, the gen-
uted in aregulargrid, should give comparable interpolations erally preferred technique. The adoption of inverse weight-
to kriging. ing for interpolation greatly simplified the automation ofthe
prediction procedure.
DefoliationPrediction Maps showing predicted defoliation and actual defolia-
A separate logistic regression model (see Hosmer and tion for the different years are presented in Fig. 3. For the
Lemeshow 1989) was developed for each prediction year. first4 years (1985 and 1989-1991), patterns ofexpected and
(This procedure is different from that ofWeseloh (in press), actual defoliation were remarkably similar, both in extent
in which one model was used for prediction in all years.) and location. For 1992-1995 this was not the case, probably
The independent variables for each 2 km by 2 km map area because so little defoliation actually occurred in the later
were: transformed, interpolated egg mass counts for the 8 years. However, the amount of defoliation, ifnot the loca-
years previous to the prediction year; coded defoliation lev- tion, appearedtobe adequatelypredicted in all years. This is
els for the 2 years before each egg mass count; elevation; shown in Fig. 4 (top), in which the numberof2 x 2 km cells
average defoliation; and percent ofthe soil region that had ofpredicted defoliation that were above 0.10 probability are
poor drainage. The last three variables did not change in the compared to the number of cells of actual defoliation for
different years, and were simply repeated for each year. each year. There is good agreement between the amount of
These variables had been found by Weseloh (in press) to be predicted and actual defoliation. However, when the corre-
important in defoliation prediction. The dependent variable lation coefficients are compared (Fig. 4, bottom), only for
was the defoliation for the summer after each egg mass the years in which defoliation was relatively extensive were
count was made, converted to if < 25% defoliation oc- correlationshigh, as is also evidentfromthemaps ofFig. 3.
curred and 1 if>25% occurred. Four two-level interactions This procedure should be useful in providing quantita-
between independent variables were then constructed by tive, spatially-explicit information about defoliation in Con-
multiplying: (1) average defoliation by interpolated egg necticut. The predictions may not be more accurate than
mass counts, (2)average defoliation by codeddefoliation for those made by experienced regulatory personnel, but the
the year before egg mass counts, (3) elevation by interpo- result in the form of a map of defoliation probabilities
lated egg mass counts, and (4) interpolated egg mass counts should be usable by more people. To aid in making the pro-
by coded defoliation for the year before egg mass counts cedure more available, the process has been automated
were done. through use ofthe GIS "IDRISi" (Eastman 1993), and cus-
To predict defoliation, parameters estimated from the tom programs written by the author. Details are given in the
fitting of the model were used with interpolated egg mass Appendix.
counts from the winter before the prediction year, coded
defoliation values from the 2 years before the prediction REFERENCESCITED
%
year, elevation, average defoliation, poor soil drainage,
and the four interactions to obtain probability estimates at Andreadis, T.G., and R.M. Weseloh. 1990. Discovery of
each 2 by 2 km area throughout Connecticut. Predictions Entomophaga maimaiga in North American gypsy moth,
PredictingDefoliationbythe GypsyMoth inConnecticut
Lymantria dispar. Proc. Natl. Acad. Sci. USA 87: 2461- Liebhold, A.M., E.E. Simons, A. Sior, and J.D. Unger.
2465. 1993b. Forecasting defoliation caused by the gypsy moth
from fieldmeasurements. Environ. Entomol. 22: 26-32.
Eastman, J.R. 1993. IDRISI Version 4.1, Clark Univer-
sity Graduate School of Geography, Worcester, Massachu- Montgomery, M.E. 1990. Role of site and insect vari-
setts. ables in forecastingdefoliationbythegypsymoth. pp. 73-84
in Watt, A.D., S.R. Leather, M.D. Hunter, and N.A.C. Kidd
Gage, S.H., T.M. Wirth, and G.A. Simmons. 1990. Pre-
[eds.]. Population Dynamics of Forest Insects. Intercept,
dicting regional gypsy moth (Lymantriidae) population
Andover, Hampshire, England. 408 pp.
trends in an expanding population using pheromone trap
catch andspatial analysis. Environ. Entomol. 19: 370-377. Weseloh, R.M., 1996. Developing and validating a
Gansner, D.A., O.W. Herrick, andM. Ticehurst. 1985. A model for predicting gypsy moth (Lepidoptera:
method forpredictinggypsymoth defoliationfromeggmass Lymantriidae), defoliation in Connecticut. J. Econ. Entomol.
(inpress).
counts. North. J. Appl. For. 2: 78-79.
Williams, D.W., RW. Fuester, W.W. Metterhouse, R.J.
Gribko, L.S., A.M. Liebhold and M.E. Hohn. 1995. Balamm, RH. Bullock and R.J. Chianese. 1991. Oak defo-
Model to predict gypsy moth (Lepidoptera: Lymantriidae)
liation and population density relationships for the gypsy
defoliation using kriging and logistic regression. Environ.
moth (Lepidoptera: Lymantriidae). J. Econ. Entomol. 84:
Entomol. 24: 529-537.
1508-1514.
Hohn, M.E., A.M. LiebholdandL.S. Gribko. 1993. Geo- Zhou, G., & A.M. Liebhold. 1995. Forecasting the spa-
statistical model for forecasting spatial dynamics ofdefolia-
tial dynamics ofgypsy moth outbreaks using cellular transi-
tion caused by the gypsy moth (Lepidoptera: Lymantriidae).
tionmodels. Landscape Ecol. 10: 177-189.
Environ. Entomol. 22: 1066-1075.
Hosmer, D.W., and S. Lemeshow. 1989. Applied Logis- ACKNOWLEDGMENTS
tic Regression. John Wiley& Sons. NewYork, 307 pp.
Isaaks, E.H. and R.M. Srivastava. 1989. Applied Geo- I wish to thank Morgan Lowry for her help in complet-
statistics. Oxford University Press, New York, Oxford. ingthisproject; Douglas Dingman forthe use ofa digitizing
561 pp. camera; and regulatory personnel in the office of Carol
Lemmon, Deputy Connecticut State Entomologist, for pro-
Liebhold, A.M., X. Xhang, M.E. Hohn, J.S. Elkinton,
viding the defoliation and egg mass data (all at The Con-
M. Ticehurst, G.L. Benzon and R.W. Campbell. 1991. Sta-
tistical analysis ofgypsy moth (Lepidoptera: Lymantriidae) nrieccktiMcuctGlAagrmiecruyl,turMaalpExLpiberrairmieannt(USntiatvieorns)i.tyI oaflsCoontnheacntkicPuatt)-
eggmasspopulations. Environ. Entomol. 20: 1407-1417.
for making available Digital Elevation Model files. This
Liebhold, A.M., RE. Rossi, and W.P. Kemp. 1993a. research was supportedby aU.S. Department ofAgriculture
Geostatistics and geographic information systems in applied Forest Service Cooperative Forest Health Program Focus
insectecology. Ann. Rev. Entomol. 38: 303-327. Fund.
ConnecticutAgricultural ExperimentStation Bulletin 938
Elevation Kriging
Inverse-
Average Defoliation Distance Weighting
Fig. 1. Maps ofConnecticut elevation in meters and av- Fig. 2. Comparison of interpolated egg mass counts in
erage defoliation from 1969-1994. In this and subsequent Connecticut for 1990 when the interpolation procedure was
maps, darkerpixels havethe highervalues. kriging(top) orinversedistanceweighting (bottom).
PredictingDefoliation bytheGypsyMoth in Connecticut
1989 1990
Predicted
Actual
Predicted
Actual
Predicted
Actual
Fig. 3. Connecticutmaps ofdefoliationpredictedfrom the logisticmodelsandactual defoliation for 1985 and 1989-1995.
ConnecticutAgricultural ExperimentStation Bulletin938
500
« 400
O Predicted
300 Defoliation
200
-Q
E
Z
100
c^. m^\
1.0
0.8
5
0.6
_o
0.4
o
o
0.2
0.0 -
1984 1986 1988 1990 1992 1994 1996
Year
Fig. 4. Number of2 km by 2 km cells where predicted defoliation probability was greater than 0.1 and number ofcells
where actual defoliation occurred in the prediction years (top), and correlation coefficients in the prediction years between
predictedandactual defoliation (bottom).
